#import fulldata from 01_accessing_data.R
# setwd("C:/Users/Diane/Dropbox/BWG Drought Experiment/Paper 1_thresholds")
library(lme4)
library(lmerTest)
library(MASS)
library(vcd)
library(fitdistrplus)
library(car)
library(knitr)
library(visreg)
library(vegan)
library(dplyr)
library(ggplot2)
library(RColorBrewer)
library(tidyr)
library(Hmisc)
library(quantreg)
library(MuMIn)
library(readr)
library(tibble)
agreement <- Agreement::agreement
fulldata<-read.csv("../00_BWGrainfall_data/Data/BWG_wide_functional_groups_ibuttons.csv")
## Warning in file(file, "rt"): cannot open file '../00_BWGrainfall_data/Data/
## BWG_wide_functional_groups_ibuttons.csv': No such file or directory
## Error in file(file, "rt"): cannot open the connection
sim<-read.csv("Data/simblank.csv", row.names=1)
## Warning in file(file, "rt"): cannot open file 'Data/simblank.csv': No such
## file or directory
## Error in file(file, "rt"): cannot open the connection
invert_traits<-read.csv("../00_BWGrainfall_data/Data/BWG_final_invertebrate_traits.csv")
## Warning in file(file, "rt"): cannot open file '../00_BWGrainfall_data/Data/
## BWG_final_invertebrate_traits.csv': No such file or directory
## Error in file(file, "rt"): cannot open the connection
#need to use package agreement function agreement to do Lin's concordance and partition acuracy and precision
#Lawrence Lin, A. S Hedayat, Bikas Sinha, Min Yang. Journal of the American Statistical Associa-
#tion. March 1, 2002, 97(457): 257-270
#=====don't skip!making new variables========
#cardoso has an "engulfing" monopelopia (Diptera 175) - switch to piercing - this has now been done
#macae had a grazer, this has now been corrected
#======Dsquared function====
Dsquared <- function(model = NULL,
obs = NULL,
pred = NULL,
family = NULL, # needed only when 'model' not provided
adjust = FALSE,
npar = NULL) { # needed only when 'model' not provided
# version 1.4 (31 Aug 2015)
model.provided <- ifelse(is.null(model), FALSE, TRUE)
if (model.provided) {
if (!("glm" %in% class(model))) stop ("'model' must be of class 'glm'.")
if (!is.null(pred)) message("Argument 'pred' ignored in favour of 'model'.")
if (!is.null(obs)) message("Argument 'obs' ignored in favour of 'model'.")
obs <- model$y
pred <- model$fitted.values
} else { # if model not provided
if (is.null(obs) | is.null(pred)) stop ("You must provide either 'obs' and 'pred', or a 'model' object of class 'glm'.")
if (length(obs) != length(pred)) stop ("'obs' and 'pred' must be of the same length (and in the same order).")
if (is.null(family)) stop ("With 'obs' and 'pred' arguments (rather than a model object), you must also specify one of two model family options: 'binomial' or 'poisson' (in quotes).")
else if (!is.character(family)) stop ("Argument 'family' must be provided as character (i.e. in quotes: 'binomial' or 'poisson').")
else if (length(family) != 1 | !(family %in% c("binomial", "poisson"))) stop ("'family' must be either 'binomial' or 'poisson' (in quotes).")
if (family == "binomial") {
if (any(!(obs %in% c(0, 1)) | pred < 0 | pred > 1)) stop ("'binomial' family implies that 'obs' data should be binary (with values 0 or 1) and 'pred' data should be bounded between 0 and 1.")
link <- log(pred / (1 - pred)) # logit
} # end if binomial
else if (family == "poisson") {
if (any(obs %%1 != 0)) stop ("'poisson' family implies that 'obs' data should consist of whole numbers.")
link <- log(pred)
} # end if poisson
model <- glm(obs ~ link, family = family)
} # end if model not provided
D2 <- (model$null.deviance - model$deviance) / model$null.deviance
if (adjust) {
if (model.provided) {
n <- length(model$y)
#p <- length(model$coefficients)
p <- attributes(logLik(model))$df
} else {
if (is.null(npar)) stop ("Adjusted D-squared from 'obs' and 'pred' values (rather than a model object) requires specifying the number of parameters in the underlying model ('npar').")
n <- length(na.omit(obs))
p <- npar
} # end if model.provided else
D2 <- 1 - ((n - 1) / (n - p)) * (1 - D2)
} # end if adjust
return (D2)
}
#==========multisite model comparisons
aic.lmx<-function(y, family, dataset)
{
m0<-glm(y~log(maxvol)+site, family=family, data = dataset)
m1<-glm(y~log(maxvol)+site+log(mu.scalar), family=family, data = dataset)
m2<-glm(y~log(maxvol)+site+log(k.scalar), family=family, data = dataset)
m3<-glm(y~log(maxvol)+site+log(mu.scalar)+I(log(mu.scalar)^2), family=family, data = dataset)
m4<-glm(y~log(maxvol)+site+log(k.scalar)+I(log(k.scalar)^2), family=family, data = dataset)
m5<-glm(y~log(maxvol)+site*log(mu.scalar), family=family, data = dataset)
m6<-glm(y~log(maxvol)+site*log(k.scalar), family=family, data = dataset)
m7<-glm(y~log(maxvol)+site*(log(mu.scalar)+I(log(mu.scalar)^2)), family=family, data = dataset)
m8<-glm(y~log(maxvol)+site*(log(k.scalar)+I(log(k.scalar)^2)), family=family, data = dataset)
m9<-glm(y~log(maxvol)+site+log(mu.scalar)*log(k.scalar), family=family, data = dataset)
m10<-glm(y~log(maxvol)+site+log(mu.scalar)*(log(k.scalar)+I(log(k.scalar)^2)), family=family, data = dataset)
m11<-glm(y~log(maxvol)+site+log(k.scalar)*(log(mu.scalar)+I(log(mu.scalar)^2)), family=family, data = dataset)
m12<-glm(y~log(maxvol)+site+(log(k.scalar)+I(log(k.scalar)^2))*(log(mu.scalar)+I(log(mu.scalar)^2)), family=family, data = dataset)
m13<-glm(y~log(maxvol)+site*log(mu.scalar)*log(k.scalar), family=family, data = dataset)
m14<-glm(y~log(maxvol)+site*log(mu.scalar)*(log(k.scalar)+I(log(k.scalar)^2)), family=family, data = dataset)
m15<-glm(y~log(maxvol)+site*log(k.scalar)*(log(mu.scalar)+I(log(mu.scalar)^2)), family=family, data = dataset)
m16<-glm(y~log(maxvol)+site*(log(k.scalar)+I(log(k.scalar)^2))*(log(mu.scalar)+I(log(mu.scalar)^2)), family=family, data = dataset)
aic.mod<-c(m0$aic, m1$aic, m2$aic, m3$aic, m4$aic, m5$aic, m6$aic, m7$aic, m8$aic, m9$aic, m10$aic,m11$aic, m12$aic, m13$aic, m14$aic, m15$aic, m16$aic)
print(aicset<-model.sel(m0, m1, m2, m3, m4, m5, m6, m7, m8, m9, m10, m11, m12, m13, m14, m15, m16))
}
aic.lmxnb<-function(y, dataset)
{
m0<-glm.nb(y~log(maxvol)+site, data = dataset)
m1<-glm.nb(y~log(maxvol)+site+log(mu.scalar), data = dataset)
m2<-glm.nb(y~log(maxvol)+site+log(k.scalar), data = dataset)
m3<-glm.nb(y~log(maxvol)+site+log(mu.scalar)+I(log(mu.scalar)^2), data = dataset)
m4<-glm.nb(y~log(maxvol)+site+log(k.scalar)+I(log(k.scalar)^2), data = dataset)
m5<-glm.nb(y~log(maxvol)+site*log(mu.scalar), data = dataset)
m6<-glm.nb(y~log(maxvol)+site*log(k.scalar), data = dataset)
m7<-glm.nb(y~log(maxvol)+site*(log(mu.scalar)+I(log(mu.scalar)^2)), data = dataset)
m8<-glm.nb(y~log(maxvol)+site*(log(k.scalar)+I(log(k.scalar)^2)), data = dataset)
m9<-glm.nb(y~log(maxvol)+site+log(mu.scalar)*log(k.scalar), data = dataset)
m10<-glm.nb(y~log(maxvol)+site+log(mu.scalar)*(log(k.scalar)+I(log(k.scalar)^2)), data = dataset)
m11<-glm.nb(y~log(maxvol)+site+log(k.scalar)*(log(mu.scalar)+I(log(mu.scalar)^2)), data = dataset)
m12<-glm.nb(y~log(maxvol)+site+(log(k.scalar)+I(log(k.scalar)^2))*(log(mu.scalar)+I(log(mu.scalar)^2)), data = dataset)
m13<-glm.nb(y~log(maxvol)+site*log(mu.scalar)*log(k.scalar), data = dataset)
m14<-glm.nb(y~log(maxvol)+site*log(mu.scalar)*(log(k.scalar)+I(log(k.scalar)^2)), data = dataset)
m15<-glm.nb(y~log(maxvol)+site*log(k.scalar)*(log(mu.scalar)+I(log(mu.scalar)^2)), data = dataset)
m16<-glm.nb(y~log(maxvol)+site*(log(k.scalar)+I(log(k.scalar)^2))*(log(mu.scalar)+I(log(mu.scalar)^2)), data = dataset)
print(aicset<-model.sel(m0, m1, m2, m3, m4, m5, m6, m7, m8, m9, m10, m11, m12, m13, m14, m15, m16))
}
aic.lmxnb.init<-function(y,init.theta, dataset)
{
m0<-glm.nb(y~log(maxvol)+site, init.theta=init.theta, data = dataset)
m1<-glm.nb(y~log(maxvol)+site+log(mu.scalar),init.theta=init.theta, data = dataset)
m2<-glm.nb(y~log(maxvol)+site+log(k.scalar),init.theta=init.theta, data = dataset)
m3<-glm.nb(y~log(maxvol)+site+log(mu.scalar)+I(log(mu.scalar)^2), init.theta=init.theta,data = dataset)
m4<-glm.nb(y~log(maxvol)+site+log(k.scalar)+I(log(k.scalar)^2), init.theta=init.theta,data = dataset)
m5<-glm.nb(y~log(maxvol)+site*log(mu.scalar), init.theta=init.theta, data = dataset)
m6<-glm.nb(y~log(maxvol)+site*log(k.scalar), init.theta=init.theta,data = dataset)
m7<-glm.nb(y~log(maxvol)+site*(log(mu.scalar)+I(log(mu.scalar)^2)), init.theta=init.theta,data = dataset)
m8<-glm.nb(y~log(maxvol)+site*(log(k.scalar)+I(log(k.scalar)^2)), init.theta=init.theta,data = dataset)
m9<-glm.nb(y~log(maxvol)+site+log(mu.scalar)*log(k.scalar), init.theta=init.theta, data = dataset)
m10<-glm.nb(y~log(maxvol)+site+log(mu.scalar)*(log(k.scalar)+I(log(k.scalar)^2)), init.theta=init.theta,data = dataset)
m11<-glm.nb(y~log(maxvol)+site+log(k.scalar)*(log(mu.scalar)+I(log(mu.scalar)^2)), init.theta=init.theta,data = dataset)
m12<-glm.nb(y~log(maxvol)+site+(log(k.scalar)+I(log(k.scalar)^2))*(log(mu.scalar)+I(log(mu.scalar)^2)), init.theta=init.theta,data = dataset)
m13<-glm.nb(y~log(maxvol)+site*log(mu.scalar)*log(k.scalar),init.theta=init.theta, data = dataset)
m14<-glm.nb(y~log(maxvol)+site*log(mu.scalar)*(log(k.scalar)+I(log(k.scalar)^2)), init.theta=init.theta,data = dataset)
m15<-glm.nb(y~log(maxvol)+site*log(k.scalar)*(log(mu.scalar)+I(log(mu.scalar)^2)), init.theta=init.theta,data = dataset)
m16<-glm.nb(y~log(maxvol)+site*(log(k.scalar)+I(log(k.scalar)^2))*(log(mu.scalar)+I(log(mu.scalar)^2)), init.theta=init.theta,data = dataset)
print(aicset<-model.sel(m0, m1, m2, m3, m4, m5, m6, m7, m8, m9, m10, m11, m12, m13, m14, m15, m16))
}
aic.lmxnb.add<-function(y, dataset)
{
m0<-glm.nb(y~log(maxvol)+site, data = dataset)
m1<-glm.nb(y~log(maxvol)+site+log(mu.scalar), data = dataset)
m2<-glm.nb(y~log(maxvol)+site+log(k.scalar), data = dataset)
m3<-glm.nb(y~log(maxvol)+site+log(mu.scalar)+I(log(mu.scalar)^2), data = dataset)
m4<-glm.nb(y~log(maxvol)+site+log(k.scalar)+I(log(k.scalar)^2), data = dataset)
m9<-glm.nb(y~log(maxvol)+site+log(mu.scalar)*log(k.scalar), data = dataset)
m10<-glm.nb(y~log(maxvol)+site+log(mu.scalar)*(log(k.scalar)+I(log(k.scalar)^2)), data = dataset)
m11<-glm.nb(y~log(maxvol)+site+log(k.scalar)*(log(mu.scalar)+I(log(mu.scalar)^2)), data = dataset)
m12<-glm.nb(y~log(maxvol)+site+(log(k.scalar)+I(log(k.scalar)^2))*(log(mu.scalar)+I(log(mu.scalar)^2)), data = dataset)
print(aicset<-model.sel(m0, m1, m2, m3, m4, m9, m10, m11, m12))
}
aic.lmx.add<-function(y, family, dataset)
{
m0<-glm(y~log(maxvol)+site, family=family, data = dataset)
m1<-glm(y~log(maxvol)+site+log(mu.scalar), family=family,data = dataset)
m2<-glm(y~log(maxvol)+site+log(k.scalar),family=family, data = dataset)
m3<-glm(y~log(maxvol)+site+log(mu.scalar)+I(log(mu.scalar)^2), family=family,data = dataset)
m4<-glm(y~log(maxvol)+site+log(k.scalar)+I(log(k.scalar)^2), family=family,data = dataset)
m9<-glm(y~log(maxvol)+site+log(mu.scalar)*log(k.scalar),family=family,data = dataset)
m10<-glm(y~log(maxvol)+site+log(mu.scalar)*(log(k.scalar)+I(log(k.scalar)^2)), family=family,data = dataset)
m11<-glm(y~log(maxvol)+site+log(k.scalar)*(log(mu.scalar)+I(log(mu.scalar)^2)), family=family,data = dataset)
m12<-glm(y~log(maxvol)+site+(log(k.scalar)+I(log(k.scalar)^2))*(log(mu.scalar)+I(log(mu.scalar)^2)), family=family,data = dataset)
print(aicset<-model.sel(m0, m1, m2, m3, m4, m9, m10, m11, m12))
}
aic.hydro<-function(y, formula, family, dataset)
{
m0<-glm(formula, family=family, data = dataset)
m1<-glm(y~log(maxvol)+site+cv.depth, family=family, data = dataset)
m2<-glm(y~log(maxvol)+site+prop.overflow.days, family=family, data = dataset)
m3<-glm(y~log(maxvol)+site+prop.driedout.days, family=family, data = dataset)
m4<-glm(y~log(maxvol)+site+mean.depth, family=family, data = dataset)
m5<-glm(y~log(maxvol)+site+long_dry, family=family, data = dataset)
m6<-glm(y~log(maxvol)+site+last_wet, family=family, data = dataset)
m7<-glm(y~log(maxvol)+site+change_cv_temp, family=family, data = dataset)
m8<-glm(y~log(maxvol)+site+change_mean_temp, family=family, data = dataset)
m17<-glm(y~log(maxvol)+site*cv.depth, family=family, data = dataset)
m18<-glm(y~log(maxvol)+site*prop.overflow.days, family=family, data = dataset)
m19<-glm(y~log(maxvol)+site*prop.driedout.days, family=family, data = dataset)
m20<-glm(y~log(maxvol)+site*mean.depth, family=family, data = dataset)
m21<-glm(y~log(maxvol)+site*long_dry, family=family, data = dataset)
m22<-glm(y~log(maxvol)+site*last_wet, family=family, data = dataset)
m23<-glm(y~log(maxvol)+site*change_cv_temp, family=family, data = dataset)
m24<-glm(y~log(maxvol)+site*change_mean_temp, family=family, data = dataset)
print(aicset<-model.sel(m0, m1, m2, m3, m4, m5, m6, m7, m8, m17,m18,m19,m20,m21,m22,m23,m24))
}
aic.hydro.add<-function(y, family, dataset)
{
m0<-glm(y~log(maxvol)+site, family=family, data = dataset)
m1<-glm(y~log(maxvol)+site+cv.depth, family=family, data = dataset)
m2<-glm(y~log(maxvol)+site+prop.overflow.days, family=family, data = dataset)
m3<-glm(y~log(maxvol)+site+prop.driedout.days, family=family, data = dataset)
m4<-glm(y~log(maxvol)+site+mean.depth, family=family, data = dataset)
m5<-glm(y~log(maxvol)+site+long_dry, family=family, data = dataset)
m6<-glm(y~log(maxvol)+site+last_wet, family=family, data = dataset)
print(aicset<-model.sel(m0, m1, m2, m3, m4, m5, m6))
}
aic.hydro.nb<-function(y, formula, dataset)
{
m0<-glm.nb(formula,data = dataset)
m1<-glm.nb(y~log(maxvol)+site+cv.depth, data = dataset)
m2<-glm.nb(y~log(maxvol)+site+prop.overflow.days, data = dataset)
m3<-glm.nb(y~log(maxvol)+site+prop.driedout.days, data = dataset)
m4<-glm.nb(y~log(maxvol)+site+mean.depth, data = dataset)
m5<-glm.nb(y~log(maxvol)+site+long_dry, data = dataset)
m6<-glm.nb(y~log(maxvol)+site+last_wet, data = dataset)
m7<-glm.nb(y~log(maxvol)+site+change_cv_temp, data = dataset)
m8<-glm.nb(y~log(maxvol)+site+change_mean_temp, data = dataset)
m17<-glm.nb(y~log(maxvol)+site*cv.depth, data = dataset)
m18<-glm.nb(y~log(maxvol)+site*prop.overflow.days, data = dataset)
m19<-glm.nb(y~log(maxvol)+site*prop.driedout.days, data = dataset)
m20<-glm.nb(y~log(maxvol)+site*mean.depth, data = dataset)
m21<-glm.nb(y~log(maxvol)+site*long_dry, data = dataset)
m22<-glm.nb(y~log(maxvol)+site*last_wet, data = dataset)
m23<-glm.nb(y~log(maxvol)+site*change_cv_temp, data = dataset)
m24<-glm.nb(y~log(maxvol)+site*change_mean_temp, data = dataset)
print(aicset<-model.sel(m0, m1, m2, m3, m4, m5, m6, m7, m8,m17,m18,m19,m20,m21,m22,m23,m24))
}
aic.hydro.nb.add<-function(y, dataset)
{
m0<-glm.nb(y~log(maxvol)+site, data = dataset)
m1<-glm.nb(y~log(maxvol)+site+cv.depth, data = dataset)
m2<-glm.nb(y~log(maxvol)+site+prop.overflow.days, data = dataset)
m3<-glm.nb(y~log(maxvol)+site+prop.driedout.days, data = dataset)
m4<-glm.nb(y~log(maxvol)+site+mean.depth, data = dataset)
m5<-glm.nb(y~log(maxvol)+site+long_dry, data = dataset)
m6<-glm.nb(y~log(maxvol)+site+last_wet, data = dataset)
print(aicset<-model.sel(m0, m1, m2, m3, m4, m5, m6))
}
aic.site<-function(y, family, dataset)
{
m0<-glm(y~log(maxvol), family=family, data = dataset)
m1<-glm(y~log(maxvol)+log(mu.scalar), family=family, data = dataset)
m2<-glm(y~log(maxvol)+log(k.scalar), family=family, data = dataset)
m3<-glm(y~log(maxvol)+log(mu.scalar)+I(log(mu.scalar)^2), family=family, data = dataset)
m4<-glm(y~log(maxvol)+log(k.scalar)+I(log(k.scalar)^2), family=family, data = dataset)
m9<-glm(y~log(maxvol)+log(mu.scalar)*log(k.scalar), family=family, data = dataset)
m10<-glm(y~log(maxvol)+log(mu.scalar)*(log(k.scalar)+I(log(k.scalar)^2)), family=family, data = dataset)
m11<-glm(y~log(maxvol)+log(k.scalar)*(log(mu.scalar)+I(log(mu.scalar)^2)), family=family, data = dataset)
m12<-glm(y~log(maxvol)+(log(k.scalar)+I(log(k.scalar)^2))*(log(mu.scalar)+I(log(mu.scalar)^2)), family=family, data = dataset)
print(aicset<-model.sel(m0, m1, m2, m3, m4, m9, m10, m11, m12))
}
aic.sitenb<-function(y, dataset)
{
m0<-glm.nb(y~log(maxvol), data = dataset)
m1<-glm.nb(y~log(maxvol)+log(mu.scalar), data = dataset)
m2<-glm.nb(y~log(maxvol)+log(k.scalar), data = dataset)
m3<-glm.nb(y~log(maxvol)+log(mu.scalar)+I(log(mu.scalar)^2), data = dataset)
m4<-glm.nb(y~log(maxvol)+log(k.scalar)+I(log(k.scalar)^2), data = dataset)
m9<-glm.nb(y~log(maxvol)+log(mu.scalar)*log(k.scalar), data = dataset)
m10<-glm.nb(y~log(maxvol)+log(mu.scalar)*(log(k.scalar)+I(log(k.scalar)^2)), data = dataset)
m11<-glm.nb(y~log(maxvol)+log(k.scalar)*(log(mu.scalar)+I(log(mu.scalar)^2)), data = dataset)
m12<-glm.nb(y~log(maxvol)+(log(k.scalar)+I(log(k.scalar)^2))*(log(mu.scalar)+I(log(mu.scalar)^2)), data = dataset)
print(aicset<-model.sel(m0, m1, m2, m3, m4, m9, m10, m11, m12))
}
aic.site.hydro.nb<-function(y, dataset)
{
m0<-glm.nb(y~log(maxvol), data = dataset)
m1<-glm.nb(y~log(maxvol)+cv.depth, data = dataset)
m2<-glm.nb(y~log(maxvol)+prop.overflow.days, data = dataset)
m3<-glm.nb(y~log(maxvol)+prop.driedout.days, data = dataset)
m4<-glm.nb(y~log(maxvol)+mean.depth, data = dataset)
m5<-glm.nb(y~log(maxvol)+long_dry, data = dataset)
m6<-glm.nb(y~log(maxvol)+last_wet, data = dataset)
print(aicset<-model.sel(m0, m1, m2, m3, m4, m5, m6))
}
aic.site.hydro<-function(y, family, dataset)
{
m0<-glm(y~log(maxvol), family=family, data = dataset)
m1<-glm(y~log(maxvol)+cv.depth, family=family, data = dataset)
m2<-glm(y~log(maxvol)+prop.overflow.days, family=family,data = dataset)
m3<-glm(y~log(maxvol)+prop.driedout.days, family=family,data = dataset)
m4<-glm(y~log(maxvol)+mean.depth, family=family, data = dataset)
m5<-glm(y~log(maxvol)+long_dry, family=family,data = dataset)
m6<-glm(y~log(maxvol)+last_wet, family=family,data = dataset)
print(aicset<-model.sel(m1, m2, m3, m4, m5, m6))
}
#rule: type 3 if sig interactions, type 2 if just sig main effects. Start with type 2 (most power), if sig int switch to type 3
#useful check for NAs
datacheck<-function(a)
{
sum(length(which((a)>0)))
}
tapply(fulldata$Odonata_bio, fulldata$site, datacheck)
## argentina cardoso colombia costarica frenchguiana
## 0 26 4 8 2
## macae puertorico
## 18 0
#===creation of custom datasets===================
fulldata$site<-as.factor(fulldata$site)
fulldata$mu.scalar.2<-fulldata$mu.scalar^2
fulldata$k.scalar.2<-fulldata$k.scalar^2
fulldata$intended.mu.2<-fulldata$intended.mu^2
fulldata$intended.k.2<-fulldata$intended.k^2
fulldata$change.k<-abs(log(fulldata$k.scalar))
fulldata$change.mu<-abs(log(fulldata$mu.scalar))
fulldata$bacteria.per.nl.final<-(fulldata$bacteria.per.ml.final/1000000)
# predator_biomass is the sum of predator_engulfer_total_biomass and
# predator_piercer_total_biomass. Check:
with(fulldata,
stopifnot(all.equal(predator_biomass,
predator_engulfer_total_biomass +
predator_piercer_total_biomass)))
# prey_biomass is the sum of prey_scraper_total_biomass,
# prey_gatherer_total_biomass, prey_shredder_total_biomass and
# prey_piercer_total_biomass and prey_filter.feeder_total_biomass
with(fulldata,
# Stop everything if prey_biomass does not equal the sum of these columns
stopifnot(all.equal(prey_biomass,
prey_scraper_total_biomass +
prey_gatherer_total_biomass +
prey_shredder_total_biomass +
prey_piercer_total_biomass +
prey_filter.feeder_total_biomass)))
# detritivore_total_biomass is a separate thing from total_prey_biomass. This is
# ONLY the scrapers, gatherers and shredders
fulldata$detritivore_total_biomass <- with(fulldata, prey_scraper_total_biomass +
prey_gatherer_total_biomass +
prey_shredder_total_biomass)
fulldata$PDratio <- with(fulldata, predator_biomass / detritivore_total_biomass)
# This next line seems unnecessary now -- we have this number as prey_biomass.
# This includes also prey which are piercing (Cecidomyiidae)
# fulldata$totalbio <- fulldata$predator_bio + fulldata$detritivore_bio + fulldata$filter.feeder_bio
# fulldata <-
full_data_temp_trophic <- fulldata %>%
# Separating taxonomic groups by their trophic position
mutate(detChiron_bio = Chironomidae_bio - Tanypodinae_bio,
predCerato_bio = Bezzia_bio + Sphaeromias_bio + Stilobezzia_bio + Culicoides_bio,
detCerato_bio = Ceratopogonidae_bio - predCerato_bio,
TipulidaeLimoniidae_bio = Tipulidae_bio + Limoniidae_bio,
filtCulicidae = Culex_bio + Wyeomyia_bio + Anopheles_bio) %>%
# deriving environmental data
mutate(exposure = prop.driedout.days * mean_temp,
# How does temperature vary from a the site average? This is equivalent
# to subtracting the site mean from mean_temp
change_mean_temp = resid(glm(mean_temp~site, family=gaussian, data=fulldata, na.action=na.exclude)),
change_cv_temp = resid(glm(cv_mean_temp~site, family=gaussian, data=fulldata, na.action=na.exclude)))
# dropping NAs in mean_temp would drop.. 30 bromeliads?!
full_data_temp_trophic %>%
filter(is.na(mean_temp))
## site_brom.id site trt.name mu.scalar k.scalar intended.mu
## 1 cardoso_DEC17 cardoso mu3k0.5 3.0 0.5 22.67
## 2 cardoso_DEC36 cardoso mu0.1k1 0.1 1.0 0.76
## 3 cardoso_DEC54 cardoso mu0.2k1 0.2 1.0 1.51
## intended.k temporal.block maxvol leaf.number mean.diam catchment.area
## 1 0.10 a 1015 33 96.0000 NA
## 2 0.19 a 1150 35 106.0000 NA
## 3 0.19 a 1225 38 105.0000 NA
## turbidity.initial oxygen.percent.initial oxygen.conc.initial ph.initial
## 1 43.67 NA NA 5.0
## 2 55.46 NA NA 5.0
## 3 56.16 NA NA 5.6
## chlorophyll.initial bacteria.per.ml..initial amoeba.per.ml.initial
## 1 -5.21016 248828.21 NA
## 2 -1.39536 337122.10 NA
## 3 -3.12324 3298980.51 NA
## algae.per.ml.initial ciliates.per.ml.initial flagellates.per.ml.initial
## 1 NA NA NA
## 2 NA NA NA
## 3 NA NA NA
## rotifers.per.ml.initial leafpack1.species1.mass.initial
## 1 NA 232.8
## 2 NA 178.4
## 3 NA 227.2
## leafpack2.species1.mass.initial leafpack1.species2.mass.initial
## 1 216.7 NA
## 2 211.5 NA
## 3 232.8 NA
## leafpack2.species2.mass.initial co2.final methane.final
## 1 NA NA NA
## 2 NA NA NA
## 3 NA NA NA
## turbidity.final oxygen.percent.final oxygen.conc.final ph.final
## 1 246.90 NA NA 5.300000
## 2 750.00 NA NA 4.866667
## 3 327.00 NA NA 4.533333
## chlorophyll.final bacteria.per.ml.final amoeba.per.ml.final
## 1 56.83440 2977911.8 NA
## 2 414.73200 7504980.0 NA
## 3 131.33520 5771209.2 NA
## algae.per.ml.final ciliates.per.ml.final flagellates.per.ml.final
## 1 NA NA NA
## 2 NA NA NA
## 3 NA NA NA
## rotifers.per.ml.final leafpack1.species1.mass.final
## 1 NA 185.0
## 2 NA 142.9
## 3 NA 175.9
## leafpack2.species1.mass.final leafpack1.species2.mass.final
## 1 160.4 NA
## 2 158.8 NA
## 3 144.8 NA
## leafpack2.species2.mass.final water.volume.final fpom.final
## 1 NA 527.0 310.8696
## 2 NA 566.0 242.0000
## 3 NA 506.5 264.0000
## n15.bromeliad.final final.bromeliad.percentn final.bromeliad.percentc
## 1 5.26276965 0.6867388 NA
## 2 14.92548502 0.6623788 NA
## 3 33.20094345 0.5370105 NA
## species1 species2 decomp sample_size_species mean.depth max.depth
## 1 0.2325663 NA 0.2325663 1 NA NA
## 2 0.2240818 NA 0.2240818 1 NA NA
## 3 0.3018996 NA 0.3018996 1 NA NA
## min.depth amplitude net_fluc tot_fluc sd.depth cv.depth wetness
## 1 NA NA NA NA NA NA NA
## 2 NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA
## prop.overflow.days prop.driedout.days long_dry long_wet n_driedout
## 1 NA NA NA NA NA
## 2 NA NA NA NA NA
## 3 NA NA NA NA NA
## n_overflow last_dry last_wet NA_NA_total_abundance NA_NA_total_biomass
## 1 NA NA NA 4 0.22784810
## 2 NA NA NA 1 0.09333333
## 3 NA NA NA 1 0.09333333
## NA_NA_total_taxa predator_engulfer_total_abundance
## 1 1 14
## 2 1 57
## 3 1 47
## predator_engulfer_total_biomass predator_engulfer_total_taxa
## 1 1.6811491 4
## 2 7.9858182 5
## 3 7.5820963 5
## predator_piercer_total_abundance predator_piercer_total_biomass
## 1 77 0.5384615
## 2 144 1.0034615
## 3 242 1.8632051
## predator_piercer_total_taxa prey_filter.feeder_total_abundance
## 1 2 30
## 2 3 21
## 3 4 14
## prey_filter.feeder_total_biomass prey_filter.feeder_total_taxa
## 1 3.562500 3
## 2 2.493750 2
## 3 1.742647 4
## prey_gatherer_total_abundance prey_gatherer_total_biomass
## 1 347 12.450200
## 2 398 15.965676
## 3 260 8.937647
## prey_gatherer_total_taxa prey_piercer_total_abundance
## 1 12 0
## 2 14 0
## 3 12 0
## prey_piercer_total_biomass prey_piercer_total_taxa
## 1 0.00000000 0
## 2 0.00000000 0
## 3 0.00000000 0
## prey_scraper_total_abundance prey_scraper_total_biomass
## 1 43 19.35000000
## 2 62 27.90000000
## 3 15 6.75000000
## prey_scraper_total_taxa prey_shredder_total_abundance
## 1 1 6
## 2 2 7
## 3 1 12
## prey_shredder_total_biomass prey_shredder_total_taxa predator_biomass
## 1 3.22526316 2 2.219611
## 2 3.76280702 1 8.989280
## 3 6.45052632 1 9.445301
## prey_biomass predator_abundance prey_abundance Coleoptera_bio
## 1 38.587963 91 426 19.3500000
## 2 50.122233 201 488 27.9000000
## 3 23.880820 289 301 6.8500000
## Diptera_bio Ephemeroptera_bio Haplotaxida_bio Lepidoptera_bio
## 1 18.411145 0.0000000 1.93670886 0
## 2 25.933856 0.0000000 0.17088608 0
## 3 20.819185 0.0000000 1.02531646 0
## Odonata_bio ord_NA_bio Podocopida_bio Trichoptera_bio Hirudinea_bio
## 1 0.4297205 0.90784810 0.00000000 0.00 0.6800000
## 2 4.5201040 0.68000000 0.00000000 0.00 0.6800000
## 3 4.2716201 0.45333333 0.00000000 0.00 0.4533333
## Neoptera_bio Oligochaeta_bio Palaeoptera_bio Podocopa_bio
## 1 37.761145 1.93670886 0.4297205 0.00000000
## 2 53.833856 0.17088608 4.5201040 0.00000000
## 3 27.669185 1.02531646 4.2716201 0.00000000
## subclass_NA_bio Aeolosomatidae_bio Anisopodidae_bio Calamoceratidae_bio
## 1 0.22784810 0 0.0000000 0.00
## 2 0.00000000 0 0.0000000 0.00
## 3 0.00000000 0 0.0000000 0.00
## Candonidae_bio Cecidomyiidae_bio Ceratopogonidae_bio Chironomidae_bio
## 1 0 0.00000000 0.291794872 9.360920
## 2 0 0.00000000 0.046794872 16.230076
## 3 0 0.00000000 0.339102564 8.495433
## Coenagrionidae_bio Corethrellidae_bio Culicidae_bio Curculionidae_bio
## 1 0.4297205 0.57142857 3.562500 0
## 2 3.8494997 2.78571429 2.493750 0
## 3 4.2716201 2.85714286 1.742647 0
## Dolichopodidae_bio Dytiscidae_bio Elateridae_bio Elmidae_bio
## 1 0 0.0000000 0.000000 0
## 2 0 0.0000000 0.000000 0
## 3 0 0.0000000 0.000000 0
## Empididae_bio Enchytraeoidae_bio Ephydridae_bio family_NA_bio
## 1 0.00000000 0 0.6240000 0.90784810
## 2 0.02500000 0 0.2106667 1.44393760
## 3 0.02500000 0 0.8160000 0.54666667
## Hydrophilidae_bio Lampyridae_bio Limnocytheridae_bio Limoniidae_bio
## 1 0.0 0.0 0.00000000 2.15017544
## 2 0.0 0.0 0.00000000 3.76280702
## 3 0.0 0.1 0.00000000 6.45052632
## Muscidae_bio Naididae_bio Periscelididae_bio Phoridae_bio
## 1 0 1.93670886 0 0.00000000
## 2 0 0.17088608 0 0.00000000
## 3 0 1.02531646 0 0.00000000
## Pseudostigmatidae_bio Psychodidae_bio Ptilodactylidae_bio
## 1 0 0.02857143 0.000
## 2 0 0.28571429 0.000
## 3 0 0.00000000 0.000
## Scatopsidae_bio Sciaridae_bio Scirtidae_bio Sphaeroceridae_bio
## 1 0.0000 0.00 19.35000000 0
## 2 0.0000 0.00 27.90000000 0
## 3 0.0000 0.00 6.75000000 0
## Stratiomyidae_bio Syrphidae_bio Tabanidae_bio Tipulidae_bio
## 1 0.74666667 0.0000 0.0 1.07508772
## 2 0.00000000 0.0000 0.0 0.00000000
## 3 0.00000000 0.0000 0.0 0.00000000
## Anophelinae_bio Calamoceratinae_bio Candoninae_bio Ceratopogoninae_bio
## 1 0.0000000 0.00 0 0.03846154
## 2 0.0000000 0.00 0 0.03846154
## 3 0.3176471 0.00 0 0.21153846
## Chioneinae_bio Chironominae_bio Copelatinae_bio Corethrellinae_bio
## 1 0.00000000 8.426753 0.00 0.57142857
## 2 0.00000000 14.974719 0.00 2.78571429
## 3 0.00000000 6.441385 0.00 2.85714286
## Culicinae_bio Dasyheleniae_bio Eristalinae_bio Forcipomyiinae_bio
## 1 3.562500 0 0.0000 0.253333333
## 2 2.493750 0 0.0000 0.008333333
## 3 1.425000 0 0.0000 0.127564103
## Hermetiinae_bio Limoniinae_bio Naidinae_bio Orthocladiinae_bio
## 1 0 2.1501754 0.0000 0.4341667
## 2 0 3.7628070 0.0000 0.3153571
## 3 0 6.4505263 0.0000 0.5273810
## Pristininae_bio Psychodinae_bio Sphaeridiinae_bio subfamily_NA_bio
## 1 0 0.0000 0.0 25.0986033
## 2 0 0.0000 0.0 33.8857043
## 3 0 0.0000 0.0 13.5346032
## Tabaninae_bio Tanypodinae_bio Timiriaseviinae_bio Tipulinae_bio
## 1 0.0 0.5000000 0.00000000 0.00000000
## 2 0.0 0.9400000 0.00000000 0.00000000
## 3 0.0 1.5266667 0.00000000 0.00000000
## Aelosoma_bio Anopheles_bio Atrichopogon_bio Aulophorus_bio Bezzia_bio
## 1 0 0.0000000 0.253333333 0 0.03846154
## 2 0 0.0000000 0.008333333 0 0.03846154
## 3 0 0.3176471 0.127564103 0 0.21153846
## Bromeliagrion_bio Candonopsis_bio Cheilotrichia_bio Chironomus_bio
## 1 0 0 0.00000000 1.4657143
## 2 0 0 0.00000000 4.1600000
## 3 0 0 0.00000000 5.1028571
## Contacyphon_bio Copelatus_bio Corethrella_bio Corynoneura_bio Culex_bio
## 1 0 0.00 0.57142857 0.2428571 3.562500
## 2 0 0.00 2.78571429 0.2714286 2.493750
## 3 0 0.00 2.85714286 0.3857143 1.425000
## Culicoides_bio Dero_bio Elpidium_bio Eristalis_bio Forcipomyia_bio
## 1 0.00000000 0.0000 0.00000000 0.0000 0.00000
## 2 0.00000000 0.0000 0.00000000 0.0000 0.00000
## 3 0.00000000 0.0000 0.00000000 0.0000 0.00000
## genus_NA_bio Haemagogus_bio Harnischia_bio Hermetia_bio Larsia_bio
## 1 24.823049 0 0.000000000 0 0.000000
## 2 30.048705 0 0.000000000 0 0.000000
## 3 9.404650 0 0.000000000 0 0.000000
## Leptagrion_bio Limnophyes_bio Mecistogaster_bio Monopelopia_bio
## 1 0.4297205 0.037142857 0 0.5000000
## 2 3.8494997 0.031428571 0 0.9400000
## 3 4.2716201 0.000000000 0 1.5266667
## Pericoma_bio Phylloicus_bio Polypedilum_bio Pristina_bio
## 1 0.0000 0.00 6.8229437 0
## 2 0.0000 0.00 10.6718615 0
## 3 0.0000 0.00 1.3147186 0
## Sphaeromias_bio Stenochironomus_bio Stibasoma_bio Stilobezzia_bio
## 1 0 0.000000000 0.0 0
## 2 0 0.000000000 0.0 0
## 3 0 0.000000000 0.0 0
## Tanytarsus_bio Tipula_bio Toxorhynchites_bio Trentepohlia_bio
## 1 0.138095238 0.00000000 0 2.1501754
## 2 0.142857143 0.00000000 0 3.7628070
## 3 0.023809524 0.00000000 0 6.4505263
## Wyeomyia_bio cv_max_temp cv_mean_temp cv_min_temp max_temp mean_temp
## 1 0.0000000 NA NA NA NA NA
## 2 0.0000000 NA NA NA NA NA
## 3 0.0000000 NA NA NA NA NA
## min_temp sd_max_temp sd_mean_temp sd_min_temp mu.scalar.2 k.scalar.2
## 1 NA NA NA NA 9.00 0.25
## 2 NA NA NA NA 0.01 1.00
## 3 NA NA NA NA 0.04 1.00
## intended.mu.2 intended.k.2 change.k change.mu bacteria.per.nl.final
## 1 513.9289 0.0100 0.6931472 1.0986123 2.9779118
## 2 0.5776 0.0361 0.0000000 2.3025851 7.5049800
## 3 2.2801 0.0361 0.0000000 1.6094379 5.7712092
## detritivore_total_biomass PDratio detChiron_bio predCerato_bio
## 1 35.025463 0.06337134 8.860920 0.03846154
## 2 47.628483 0.18873748 15.290076 0.03846154
## 3 22.138173 0.42665225 6.968766 0.21153846
## detCerato_bio TipulidaeLimoniidae_bio filtCulicidae exposure
## 1 0.253333333 3.22526316 3.562500 NA
## 2 0.008333333 3.76280702 2.493750 NA
## 3 0.127564103 6.45052632 1.742647 NA
## change_mean_temp change_cv_temp shredder_bio filter.feeder_bio
## 1 NA NA 3.22526316 3.562500
## 2 NA NA 3.76280702 2.493750
## 3 NA NA 6.45052632 1.742647
## scraper_bio gatherer_bio engulfer_bio piercer_bio totalbio
## 1 19.35000000 12.450200 1.6811491 0.00000000 40.807574
## 2 27.90000000 15.965676 7.9858182 0.00000000 59.111513
## 3 6.75000000 8.937647 7.5820963 0.00000000 33.326122
## resid.decomp resid.shr resid.scraper resid.tb resid.ff.hydro
## 1 -0.0035969884 0.10631221 0.201349970 -0.1264158209 0.10631221
## 2 -0.0143029844 0.29295578 0.517816695 0.2959836320 0.29295578
## 3 0.0608534911 0.98905191 -1.115647527 -0.6595539793 0.98905191
## resid.sc.hydro resid.tb.hydro scaled.n15.bromeliad.final sqrt.decomp
## 1 0.201349970 -0.1264158209 1.3208169 0.4822513
## 2 0.517816695 0.2959836320 1.4442118 0.4733728
## 3 -1.115647527 -0.6595539793 1.5715176 0.5494539
## log.co2.final engulfer.sens.num engulfer.sens.denom engulfer.sens.index
## 1 NA 0 1.6811491 0
## 2 NA 0 7.3152140 0
## 3 NA 0 7.5820963 0
## gatherer.sens.num gatherer.sens.denom gatherer.sens.index
## 1 0 12.450200 0
## 2 0 15.965676 0
## 3 0 8.937647 0
## piercer.sens.num piercer.sens.denom piercer.sens.index all.sens.num
## 1 0 0.5384615 0 0
## 2 0 1.0034615 0 0
## 3 0 1.7632051 0 0
## all.sens.denom all.sens.index
## 1 40.807574 0
## 2 58.440909 0
## 3 33.226122 0
## [ reached getOption("max.print") -- omitted 32 rows ]
full_data_temp_trophic %>%
group_by(site) %>%
count
## # A tibble: 7 × 2
## site n
## <fctr> <int>
## 1 argentina 30
## 2 cardoso 30
## 3 colombia 30
## 4 costarica 30
## 5 frenchguiana 30
## 6 macae 30
## 7 puertorico 30
# renaming fulldata to match downstream analysis ---------------------------------------------------
## from here, rename things just to permit the script to run!
fulldata <- full_data_temp_trophic %>% ungroup %>% as.data.frame
# creating new columns that match names to old ones
fulldata <- fulldata %>%
mutate(shredder_bio = prey_shredder_total_biomass,
filter.feeder_bio = prey_filter.feeder_total_biomass,
scraper_bio = prey_scraper_total_biomass,
gatherer_bio = prey_gatherer_total_biomass,
engulfer_bio = predator_engulfer_total_biomass,
piercer_bio = prey_piercer_total_biomass,
totalbio = prey_biomass + predator_biomass) # Is that the right equation?? does it leave out NA
# Continue DS analysis ----------------------------------------------------
temptruedata<-filter(fulldata, mean_temp%nin%NA)
temphydrotruedata<-filter(temptruedata, cv.depth%nin%NA)
noleakydatatemp<-filter(temptruedata, site_brom.id%nin%c("macae_B24", "macae_B22", "macae_B9", "macae_B2", "macae_B11", "macae_B41", "argentina_15"))
noleakydata<-filter(fulldata, site_brom.id%nin%c("macae_B24", "macae_B22", "macae_B9", "macae_B2", "macae_B11", "macae_B41", "argentina_15"))
nocadata<-subset(fulldata,site!="cardoso")
nocadata$resid.driedout<-resid(glm(sqrt(prop.driedout.days)~maxvol+site*log(mu.scalar)*log(k.scalar), family=gaussian, data=nocadata))
nocadata$resid.overflow<-resid(glm(sqrt(prop.overflow.days)~maxvol+site*log(mu.scalar)*log(k.scalar), family=gaussian, data=nocadata))
nocadata$resid.cvdepth<-resid(glm(log(cv.depth)~maxvol+site*log(mu.scalar)*log(k.scalar), family=gaussian, data=nocadata))
ardata<-subset(fulldata,site=="argentina")
cadata<-subset(fulldata,site=="cardoso")
codata<-subset(fulldata,site=="colombia")
crdata<-subset(fulldata,site=="costarica")
fgdata<-subset(fulldata,site=="frenchguiana")
madata<-subset(fulldata,site=="macae")
prdata<-subset(fulldata,site=="puertorico")
noargdata<-subset(fulldata,site!="argentina")
noargmacdata<-subset(noargdata,site!="macae")
noargcodata<-subset(noargdata,site!="colombia" )
noargcadata<-subset(noargdata,site!="cardoso" )
noargprdata<-subset(noargdata,site!="puertorico" )
noargcaprdata<-subset(noargcadata,site!="puertorico" )
noargcocrdata<-subset(noargcodata,site!="costarica" )
nocodata<-subset(fulldata,site!="colombia")
nocoprdata<-subset(nocodata,site!="puertorico")
nofgdata<-subset(fulldata,site!="frenchguiana")
nocafgdata<-subset(nocadata,site!="frenchguiana")
nocacodata<-subset(nocadata,site!="colombia")
nocaprdata<-subset(nocadata,site!="puertorico")
nocacoprdata<-subset(nocacodata,site!="puertorico")
nococrdata<-subset(nocodata,site!="costarica")
nococrprdata<-subset(nococrdata,site!="puertorico")
nocacocrdata<-subset(nocacodata,site!="costarica")
noprdata<-subset(fulldata,site!="puertorico")
nomadata<-subset(fulldata,site!="macae")
noargcacodata<-subset(nocacodata,site!="argentina" )
camadata<-rbind(cadata, madata)
cafgmadata<-rbind(cadata, fgdata, madata)
camaprdata<-rbind(cadata, madata, prdata)
argcacodata<-rbind(ardata, cadata, codata)
cocrdata<-rbind(codata, crdata)
argcrdata<-rbind(ardata, crdata)
cafgdata<-rbind(cadata, fgdata)
cacrmadata<-rbind(cadata, crdata, madata)
maprdata<-rbind(madata, prdata)
nococr140data<-nococrdata[-140,]#working, corrects filter feeders
no67185data<-fulldata[-(c(67,185)),] #working
noargco13data<-noargcodata[-c(13, 127, 129),]
no126data<-fulldata[-126,]#working
noargco123data<-noargcodata[-123,]
noargco123datatemp<-filter(noargco123data, mean_temp%nin%NA)
nocaco170173<-nocacodata[-c(113,110),]
target<-c("67","61","66","68","90","79","61")
nocafgdata<-rownames_to_column(nocafgdata, "row")
nocafgclean<-filter(nocafgdata, row%nin%target)
###==lets make this flow
#an AICc approach
response<-as.vector(c("Decomposition", "Nitrogen uptake", "CO2 flux", "Shredder", "Filter feeder", "Scraper", "Gatherer", "Engulfer", "Piercer", "Bacterial density", "Total Invertebrates"))
aic.percent<-as.data.frame(response)
#decomp - rain
aic.lmx(sqrt(fulldata$decomp), gaussian, fulldata) #m2, m0, m9
## Model selection table
## (Int) log(mxv) sit log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m2 0.6241 0.01828 + 0.011420
## m0 0.6451 0.01463 +
## m9 0.6271 0.01782 + 0.001070 0.008479
## m4 0.6243 0.01807 + 0.011400 0.0033000
## m3 0.6531 0.01416 + -0.004861 -0.005473
## m1 0.6441 0.01485 + 0.001013
## m11 0.6370 0.01696 + -0.004775 0.007221 -0.005443
## m10 0.6275 0.01770 + 0.004578 0.008467 0.0006586
## m5 0.6366 0.01643 + 0.007292
## m12 0.6249 0.01795 + 0.005983 0.007349 0.001306 0.0198000
## m6 0.6525 0.01335 + 0.012500
## m7 0.6490 0.01649 + -0.007767 -0.014070
## m8 0.6323 0.01737 + 0.012610 -0.0091760
## m13 0.6615 0.01209 + 0.006987 0.010130
## m14 0.6211 0.02000 + 0.015920 0.010460 -0.0157000
## m15 0.6643 0.01385 + -0.008001 0.023890 -0.014110
## m16 0.5944 0.02751 + -0.002535 0.026010 -0.017960 -0.0278700
## log(mu.scl):sit log(k.scl):sit I(log(mu.scl)^2):sit
## m2
## m0
## m9
## m4
## m3
## m1
## m11
## m10
## m5 +
## m12
## m6 +
## m7 + +
## m8 +
## m13 + +
## m14 + +
## m15 + + +
## m16 + + +
## I(log(k.scl)^2):sit log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m2
## m0
## m9 -0.011820
## m4
## m3
## m1
## m11 -0.010460
## m10 -0.011830 -0.01096
## m5
## m12 -0.010480 -0.03349
## m6
## m7
## m8 +
## m13 -0.009569
## m14 + -0.009052 -0.02614
## m15 -0.025540
## m16 + -0.026450 -0.01329
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2
## m2
## m0
## m9
## m4
## m3
## m1
## m11 0.001284
## m10
## m5
## m12 0.001255 -0.02104
## m6
## m7
## m8
## m13
## m14
## m15 -0.015030
## m16 -0.016710 0.01275
## log(k.scl):log(mu.scl):sit I(log(k.scl)^2):log(mu.scl):sit
## m2
## m0
## m9
## m4
## m3
## m1
## m11
## m10
## m5
## m12
## m6
## m7
## m8
## m13 +
## m14 + +
## m15 +
## m16 + +
## I(log(mu.scl)^2):log(k.scl):sit I(log(k.scl)^2):I(log(mu.scl)^2):sit
## m2
## m0
## m9
## m4
## m3
## m1
## m11
## m10
## m5
## m12
## m6
## m7
## m8
## m13
## m14
## m15 +
## m16 + +
## df logLik AICc delta weight
## m2 10 305.593 -590.1 0.00 0.278
## m0 9 304.268 -589.6 0.44 0.222
## m9 12 307.290 -589.0 1.08 0.161
## m4 11 305.611 -587.9 2.19 0.093
## m3 11 305.429 -587.5 2.56 0.077
## m1 10 304.305 -587.5 2.57 0.077
## m11 14 308.452 -586.8 3.33 0.053
## m10 14 307.539 -584.9 5.16 0.021
## m5 16 309.138 -583.5 6.62 0.010
## m12 17 309.589 -582.0 8.09 0.005
## m6 16 307.747 -580.7 9.40 0.003
## m7 23 314.379 -576.8 13.26 0.000
## m8 23 313.952 -576.0 14.11 0.000
## m13 30 320.546 -570.7 19.38 0.000
## m14 44 329.751 -547.5 42.58 0.000
## m15 44 329.450 -546.9 43.18 0.000
## m16 65 342.630 -495.7 94.40 0.000
## Models ranked by AICc(x)
#m2<-glm(sqrt(decomp)~log(maxvol)+site+log(k.scalar), family = gaussian, data = fulldata) confirms k not sig
bestdecomp<-glm(sqrt(decomp)~log(maxvol)+site, family=gaussian, data=fulldata)
fulldata$resid.decomp<-resid(glm(sqrt(decomp)~log(maxvol)+site,data=fulldata, na.action=na.exclude))
bestdecomp.r<-glm(resid.decomp~log(k.scalar), family=gaussian, data = fulldata)
par(mfrow=c(2,2)); plot(bestdecomp.r)
aic.percent$draintrue[aic.percent$response=="Decomposition"]<-Dsquared(bestdecomp.r, adjust=TRUE)#0
aic.percent$drainfalse[aic.percent$response=="Decomposition"]<-Dsquared(bestdecomp.r, adjust=FALSE)#0
aic.percent$raintype[aic.percent$response=="Decomposition"]<-"ns"
#n15 - rain
aic.lmx((no126data$n15.bromeliad.final+4)^0.125, gaussian, no126data)#m8 = site x k +k2
## Model selection table
## (Int) log(mxv) sit log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m8 1.861 -0.051950 + 0.06526 -0.144700
## m5 1.545 -0.003448 + 0.0398400
## m0 1.572 -0.009963 +
## m2 1.594 -0.013660 + -0.01148
## m6 1.667 -0.026460 + 0.06594
## m1 1.573 -0.010150 + -0.0008423
## m4 1.592 -0.011820 + -0.01125 -0.028790
## m3 1.566 -0.009570 + 0.0038960 0.004419
## m9 1.596 -0.014140 + -0.0009091 -0.01266
## m11 1.598 -0.015040 + 0.0036750 -0.02426 0.004292
## m10 1.595 -0.012200 + 0.0025480 -0.01240 -0.031360
## m7 1.544 -0.002645 + 0.0356900 -0.003921
## m13 1.616 -0.015920 + 0.0389600 0.07236
## m12 1.580 -0.011640 + 0.0168600 -0.02379 0.013350 -0.005642
## m14 1.769 -0.033890 + 0.0473300 0.07159 -0.152900
## m15 1.614 -0.014830 + 0.0346100 0.05514 -0.004128
## m16 1.739 -0.027290 + 0.0383300 0.05321 -0.009049 -0.166800
## log(mu.scl):sit log(k.scl):sit I(log(mu.scl)^2):sit
## m8 +
## m5 +
## m0
## m2
## m6 +
## m1
## m4
## m3
## m9
## m11
## m10
## m7 + +
## m13 + +
## m12
## m14 + +
## m15 + + +
## m16 + + +
## I(log(k.scl)^2):sit log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m8 +
## m5
## m0
## m2
## m6
## m1
## m4
## m3
## m9 -0.004588
## m11 0.008856
## m10 -0.004545 -0.01070
## m7
## m13 0.025040
## m12 0.008793 -0.04083
## m14 + 0.023870 -0.03009
## m15 0.045400
## m16 + 0.046230 -0.01505
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2
## m8
## m5
## m0
## m2
## m6
## m1
## m4
## m3
## m9
## m11 0.01258
## m10
## m7
## m13
## m12 0.01247 -0.02816
## m14
## m15 0.01895
## m16 0.02048 0.01470
## log(k.scl):log(mu.scl):sit I(log(k.scl)^2):log(mu.scl):sit
## m8
## m5
## m0
## m2
## m6
## m1
## m4
## m3
## m9
## m11
## m10
## m7
## m13 +
## m12
## m14 + +
## m15 +
## m16 + +
## I(log(mu.scl)^2):log(k.scl):sit I(log(k.scl)^2):I(log(mu.scl)^2):sit
## m8
## m5
## m0
## m2
## m6
## m1
## m4
## m3
## m9
## m11
## m10
## m7
## m13
## m12
## m14
## m15 +
## m16 + +
## df logLik AICc delta weight
## m8 23 202.097 -352.2 0.00 0.593
## m5 16 192.109 -349.4 2.84 0.143
## m0 9 183.740 -348.6 3.65 0.096
## m2 10 184.163 -347.2 5.01 0.048
## m6 16 190.866 -346.9 5.33 0.041
## m1 10 183.749 -346.4 5.84 0.032
## m4 11 184.604 -345.9 6.36 0.025
## m3 11 183.981 -344.6 7.60 0.013
## m9 12 184.251 -342.9 9.32 0.006
## m11 14 185.082 -340.0 12.23 0.001
## m10 14 184.759 -339.4 12.87 0.001
## m7 23 194.134 -336.3 15.93 0.000
## m13 30 203.023 -335.6 16.63 0.000
## m12 17 186.081 -335.0 17.27 0.000
## m14 44 222.685 -333.2 19.00 0.000
## m15 44 206.615 -301.1 51.14 0.000
## m16 65 230.625 -271.2 80.98 0.000
## Models ranked by AICc(x)
bestnit<-glm((n15.bromeliad.final+4)^0.125~log(maxvol)+site*(log(k.scalar)+I(log(k.scalar)^2)), family=gaussian, data = no126data)
#k and k2 sig, but not in macae and french guiana
no126data$resid.nit<-resid(glm((n15.bromeliad.final+4)^0.125~log(maxvol)+site,data=no126data, na.action=na.exclude))
aic.percent$draintrue[aic.percent$response=="Nitrogen uptake"]<-Dsquared(glm(resid.nit~site*(log(k.scalar)+I(log(k.scalar)^2)),family=gaussian, no126data), adjust=TRUE) #0.05186261
aic.percent$drainfalse[aic.percent$response=="Nitrogen uptake"]<-Dsquared(glm(resid.nit~site*(log(k.scalar)+I(log(k.scalar)^2)),family=gaussian, no126data), adjust=FALSE) #0.05186261
aic.percent$raintype[aic.percent$response=="Nitrogen uptake"]<-"contingent"
#co2 - rain
aic.lmx(log(nocaprdata$co2.final), gaussian, nocaprdata)#m0, m4, m1
## Model selection table
## (Int) log(mxv) sit log(mu.scl) log(k.scl) log(mu.scl)^2
## m0 0.248000 0.03339 +
## m4 0.396700 0.02244 + -0.02365
## m1 0.261400 0.03028 + -0.018220
## m2 0.299700 0.02442 + -0.02329
## m3 0.155900 0.04170 + 0.030380 0.04394
## m9 0.283700 0.02643 + -0.018070 -0.01484
## m10 0.475200 0.01124 + 0.047620 -0.01592
## m5 0.277800 0.02719 + -0.023920
## m11 0.223600 0.02995 + 0.030080 -0.06804 0.04397
## m6 0.185000 0.04431 + -0.06768
## m12 0.458800 0.01188 + 0.062660 -0.07081 0.01386
## m7 0.219400 0.03426 + -0.002525 0.01952
## m8 0.309600 0.02916 + -0.06809
## m13 0.039040 0.06872 + -0.021000 -0.05760
## m14 0.264900 0.03747 + 0.012610 -0.05893
## m15 -0.005796 0.07332 + 0.000934 -0.04531 0.02018
## m16 0.416100 0.02072 + -0.054050 -0.05345 -0.06062
## log(k.scl)^2 log(mu.scl):sit log(k.scl):sit I(log(mu.scl)^2):sit
## m0
## m4 -0.2671
## m1
## m2
## m3
## m9
## m10 -0.3218
## m5 +
## m11
## m6 +
## m12 -0.4025
## m7 + +
## m8 -0.1164 +
## m13 + +
## m14 -0.1442 + +
## m15 + + +
## m16 -0.3703 + + +
## I(log(k.scl)^2):sit log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m4
## m1
## m2
## m3
## m9 0.03234
## m10 0.03159 -0.19430
## m5
## m11 0.09191
## m6
## m12 0.09193 -0.09827
## m7
## m8 +
## m13 0.03854
## m14 + 0.03650 -0.11180
## m15 0.02462
## m16 + 0.02813 0.15710
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2
## m0
## m4
## m1
## m2
## m3
## m9
## m10
## m5
## m11 0.054980
## m6
## m12 0.056030 0.0891
## m7
## m8
## m13
## m14
## m15 -0.013270
## m16 -0.006796 0.2495
## log(k.scl):log(mu.scl):sit I(log(k.scl)^2):log(mu.scl):sit
## m0
## m4
## m1
## m2
## m3
## m9
## m10
## m5
## m11
## m6
## m12
## m7
## m8
## m13 +
## m14 + +
## m15 +
## m16 + +
## I(log(mu.scl)^2):log(k.scl):sit I(log(k.scl)^2):I(log(mu.scl)^2):sit
## m0
## m4
## m1
## m2
## m3
## m9
## m10
## m5
## m11
## m6
## m12
## m7
## m8
## m13
## m14
## m15 +
## m16 + +
## df logLik AICc delta weight
## m0 7 -71.324 157.5 0.00 0.389
## m4 9 -69.903 159.3 1.72 0.165
## m1 8 -71.172 159.5 1.96 0.146
## m2 8 -71.256 159.7 2.13 0.134
## m3 9 -70.279 160.0 2.47 0.113
## m9 10 -70.946 163.7 6.14 0.018
## m10 12 -68.735 164.0 6.51 0.015
## m5 12 -69.238 165.1 7.52 0.009
## m11 12 -69.575 165.7 8.19 0.006
## m6 12 -70.830 168.2 10.70 0.002
## m12 15 -67.130 168.3 10.79 0.002
## m7 17 -67.391 174.1 16.52 0.000
## m8 17 -69.281 177.8 20.30 0.000
## m13 22 -67.076 187.3 29.73 0.000
## m14 31 -63.596 208.6 51.10 0.000
## m15 32 -64.178 213.3 55.73 0.000
## m16 45 -58.682 254.4 96.87 0.000
## Models ranked by AICc(x)
bestco2<-glm(log(co2.final)~log(maxvol)+site, gaussian, nocaprdata)
nocaprdata$resid.co2<-resid(glm(log(nocaprdata$co2.final)~log(maxvol)+site,data=nocaprdata, na.action=na.exclude))
aic.percent$draintrue[aic.percent$response=="CO2 flux"]<-Dsquared(glm(resid.co2~(log(k.scalar)+I(log(k.scalar)^2)),family=gaussian, nocaprdata), adjust=TRUE) #0
aic.percent$drainfalse[aic.percent$response=="CO2 flux"]<-Dsquared(glm(resid.co2~(log(k.scalar)+I(log(k.scalar)^2)),family=gaussian, nocaprdata), adjust=FALSE) #0
aic.percent$raintype[aic.percent$response=="CO2 flux"]<-"ns"
#shredder - rain
aic.lmxnb(round(fulldata$shredder_bio*10), fulldata)#m4 (k+k2) m2
## Model selection table
## (Int) log(mxv) sit log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m4 1.630 0.16120 + 0.27230 -0.5773
## m2 1.531 0.15020 + 0.27070
## m0 1.931 0.07928 +
## m9 1.664 0.12220 + -0.07234 0.28610
## m10 1.721 0.13800 + -0.12050 0.28780 -0.5251
## m1 2.056 0.05323 + -0.07023
## m3 2.106 0.05337 + -0.11470 -0.04132
## m11 1.711 0.12320 + -0.11910 0.30080 -0.04350
## m12 1.971 0.11450 + -0.27020 0.29400 -0.12800 -0.7432
## m6 1.054 0.23250 + 0.03067
## m5 2.238 0.01958 + -0.10920
## m7 2.044 -0.01266 + 0.31450 0.36230
## m8 1.518 0.20820 + 0.03169 -1.1180
## m13 1.536 0.14220 + -0.09844 0.07819
## m15 1.289 0.11830 + 0.33790 0.23020 0.36800
## m14 2.388 0.04840 + -0.10430 0.08044 -1.0900
## m16 1.481 0.18010 + 0.15030 0.24330 0.18250 -2.0350
## log(mu.scl):sit log(k.scl):sit I(log(mu.scl)^2):sit
## m4
## m2
## m0
## m9
## m10
## m1
## m3
## m11
## m12
## m6 +
## m5 +
## m7 + +
## m8 +
## m13 + +
## m15 + + +
## m14 + +
## m16 + + +
## I(log(k.scl)^2):sit log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m4
## m2
## m0
## m9 0.06498
## m10 0.06403 0.164900
## m1
## m3
## m11 0.04839
## m12 0.05531 0.476000
## m6
## m5
## m7
## m8 +
## m13 0.11320
## m15 -0.10780
## m14 + 0.11390 -0.006466
## m16 + -0.14320 0.695500
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2
## m4
## m2
## m0
## m9
## m10
## m1
## m3
## m11 -0.014970
## m12 -0.008782 0.2677
## m6
## m5
## m7
## m8
## m13
## m15 -0.129600
## m14
## m16 -0.138400 0.7172
## log(k.scl):log(mu.scl):sit I(log(k.scl)^2):log(mu.scl):sit
## m4
## m2
## m0
## m9
## m10
## m1
## m3
## m11
## m12
## m6
## m5
## m7
## m8
## m13 +
## m15 +
## m14 + +
## m16 + +
## I(log(mu.scl)^2):log(k.scl):sit I(log(k.scl)^2):I(log(mu.scl)^2):sit
## m4
## m2
## m0
## m9
## m10
## m1
## m3
## m11
## m12
## m6
## m5
## m7
## m8
## m13
## m15 +
## m14
## m16 + +
## family init.theta df logLik AICc delta weight
## m4 NB(1.2825) 1.28 11 -820.586 1664.5 0.00 0.567
## m2 NB(1.251) 1.25 10 -822.660 1666.4 1.92 0.217
## m0 NB(1.2209) 1.22 9 -825.070 1669.0 4.53 0.059
## m9 NB(1.2584) 1.26 12 -821.867 1669.3 4.81 0.051
## m10 NB(1.2919) 1.29 14 -819.682 1669.5 5.01 0.046
## m1 NB(1.2264) 1.23 10 -824.475 1670.1 5.55 0.035
## m3 NB(1.2306) 1.23 11 -824.268 1671.9 7.36 0.014
## m11 NB(1.263) 1.26 14 -821.624 1673.4 8.90 0.007
## m12 NB(1.3015) 1.3 17 -819.023 1675.2 10.73 0.003
## m6 NB(1.2617) 1.26 16 -821.758 1678.3 13.83 0.001
## m5 NB(1.2454) 1.25 16 -822.934 1680.7 16.18 0.000
## m7 NB(1.326) 1.33 23 -817.211 1686.4 21.85 0.000
## m8 NB(1.3094) 1.31 23 -818.462 1688.9 24.35 0.000
## m13 NB(1.3042) 1.3 30 -818.413 1707.2 42.71 0.000
## m15 NB(1.4281) 1.43 44 -809.501 1731.0 66.50 0.000
## m14 NB(1.3857) 1.39 44 -812.992 1738.0 73.48 0.000
## m16 NB(1.6521) 1.65 65 -796.229 1782.0 117.54 0.000
## Abbreviations:
## family: NB(1.2209) = 'Negative Binomial(1.2209)',
## NB(1.2264) = 'Negative Binomial(1.2264)',
## NB(1.2306) = 'Negative Binomial(1.2306)',
## NB(1.2454) = 'Negative Binomial(1.2454)',
## NB(1.251) = 'Negative Binomial(1.251)',
## NB(1.2584) = 'Negative Binomial(1.2584)',
## NB(1.2617) = 'Negative Binomial(1.2617)',
## NB(1.263) = 'Negative Binomial(1.263)',
## NB(1.2825) = 'Negative Binomial(1.2825)',
## NB(1.2919) = 'Negative Binomial(1.2919)',
## NB(1.3015) = 'Negative Binomial(1.3015)',
## NB(1.3042) = 'Negative Binomial(1.3042)',
## NB(1.3094) = 'Negative Binomial(1.3094)',
## NB(1.326) = 'Negative Binomial(1.326)',
## NB(1.3857) = 'Negative Binomial(1.3857)',
## NB(1.4281) = 'Negative Binomial(1.4281)',
## NB(1.6521) = 'Negative Binomial(1.6521)'
## Models ranked by AICc(x)
bestsh<-glm.nb(round(shredder_bio*10)~log(maxvol)+site+log(k.scalar)+I(log(k.scalar)^2), data = fulldata)#yes both k and k2 very sig
aic.lmx(round(fulldata$shredder_bio*10), family=negative.binomial(theta = 1.6521), fulldata)#m4 (k+k2)
## Model selection table
## (Int) log(mxv) sit log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m4 1.646 0.158500 + 0.27000 -0.5833
## m2 1.527 0.150700 + 0.26790
## m10 1.725 0.137700 + -0.11800 0.28480 -0.5315
## m9 1.640 0.126400 + -0.06957 0.28270
## m0 1.906 0.083750 +
## m1 2.021 0.059560 + -0.06724
## m3 2.092 0.056580 + -0.11500 -0.0444
## m11 1.715 0.123300 + -0.11960 0.28950 -0.0466
## m12 1.989 0.112200 + -0.26820 0.28430 -0.1287 -0.7434
## m6 1.076 0.228500 + 0.03080
## m5 2.194 0.027170 + -0.10940
## m7 2.000 -0.004542 + 0.31090 0.3594
## m8 1.544 0.203400 + 0.03195 -1.1160
## m13 1.519 0.145200 + -0.09889 0.07830
## m15 1.308 0.115600 + 0.33350 0.22630 0.3650
## m14 2.391 0.047800 + -0.10460 0.08083 -1.0900
## m16 1.482 0.180100 + 0.15030 0.24330 0.1825 -2.0350
## log(mu.scl):sit log(k.scl):sit I(log(mu.scl)^2):sit
## m4
## m2
## m10
## m9
## m0
## m1
## m3
## m11
## m12
## m6 +
## m5 +
## m7 + +
## m8 +
## m13 + +
## m15 + + +
## m14 + +
## m16 + + +
## I(log(k.scl)^2):sit log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m4
## m2
## m10 0.06162 0.164700
## m9 0.06261
## m0
## m1
## m3
## m11 0.05450
## m12 0.06048 0.469100
## m6
## m5
## m7
## m8 +
## m13 0.11400
## m15 -0.10310
## m14 + 0.11470 -0.007264
## m16 + -0.14320 0.695600
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2
## m4
## m2
## m10
## m9
## m0
## m1
## m3
## m11 -0.006360
## m12 -0.001182 0.2612
## m6
## m5
## m7
## m8
## m13
## m15 -0.126700
## m14
## m16 -0.138400 0.7172
## log(k.scl):log(mu.scl):sit I(log(k.scl)^2):log(mu.scl):sit
## m4
## m2
## m10
## m9
## m0
## m1
## m3
## m11
## m12
## m6
## m5
## m7
## m8
## m13 +
## m15 +
## m14 + +
## m16 + +
## I(log(mu.scl)^2):log(k.scl):sit I(log(k.scl)^2):I(log(mu.scl)^2):sit
## m4
## m2
## m10
## m9
## m0
## m1
## m3
## m11
## m12
## m6
## m5
## m7
## m8
## m13
## m15 +
## m14
## m16 + +
## df logLik AICc delta weight
## m4 10 -823.323 1667.8 0.00 0.700
## m2 9 -826.022 1670.9 3.19 0.142
## m10 13 -822.261 1672.4 4.63 0.069
## m9 11 -825.089 1673.5 5.76 0.039
## m0 8 -829.100 1674.9 7.17 0.019
## m1 9 -828.386 1675.7 7.92 0.013
## m3 10 -828.073 1677.3 9.50 0.006
## m11 13 -824.742 1677.3 9.59 0.006
## m12 16 -821.432 1677.7 9.93 0.005
## m6 15 -824.904 1682.3 14.53 0.000
## m5 15 -826.427 1685.3 17.58 0.000
## m7 22 -819.249 1687.9 20.16 0.000
## m8 22 -820.749 1690.9 23.16 0.000
## m13 29 -820.800 1709.3 41.52 0.000
## m15 43 -810.367 1729.5 61.78 0.000
## m14 43 -814.263 1737.3 69.57 0.000
## m16 64 -796.229 1777.8 110.09 0.000
## Models ranked by AICc(x)
fulldata$resid.shr<-resid(glm.nb(round(shredder_bio*10)~log(maxvol)+site, data=fulldata, na.action=na.exclude))
aic.percent$draintrue[aic.percent$response=="Shredder"]<-Dsquared(glm(resid.shr~log(k.scalar)+I(log(k.scalar)^2),family=gaussian, fulldata), adjust=TRUE) #0.0467
aic.percent$drainfalse[aic.percent$response=="Shredder"]<-Dsquared(glm(resid.shr~log(k.scalar)+I(log(k.scalar)^2),family=gaussian, fulldata), adjust=FALSE) #0.0467
aic.percent$raintype[aic.percent$response=="Shredder"]<-"general"
#filter feeder- rain
aic.lmxnb(round(nococr140data$filter.feeder_bio*100), nococr140data)#m7 = site x mu+mu2
## Warning: glm.fit: algorithm did not converge
## Model selection table
## (Int) log(mxv) sit log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m7 -4.700 1.3930 + 0.78020 -1.12700
## m5 -4.920 1.3200 + 1.17300
## m13 -8.774 1.9290 + 1.34300 1.5540
## m2 -3.444 1.0600 + 0.3152
## m0 -2.476 0.9074 +
## m4 -3.419 1.0770 + 0.3192 -0.40800
## m6 -4.394 1.2040 + 1.2480
## m1 -2.437 0.8968 + 0.09017
## m9 -3.602 1.0840 + 0.10560 0.2876
## m3 -2.017 0.8383 + -0.02022 -0.10150
## m12 -4.945 1.2910 + 0.49030 0.4292 0.23480 0.48260
## m11 -3.175 1.0220 + 0.01104 0.3796 -0.08853
## m10 -3.764 1.1350 + 0.19240 0.2872 -0.46160
## m15 -7.782 1.8590 + 1.08800 1.6160 -1.07500
## m8 -5.954 1.5240 + 1.3240 -1.03500
## m14 -10.250 2.1690 + 1.99500 1.5400 0.01124
## m16 -8.987 2.0390 + 2.07300 1.7230 -1.14000 0.33040
## log(mu.scl):sit log(k.scl):sit I(log(mu.scl)^2):sit
## m7 + +
## m5 +
## m13 + +
## m2
## m0
## m4
## m6 +
## m1
## m9
## m3
## m12
## m11
## m10
## m15 + + +
## m8 +
## m14 + +
## m16 + + +
## I(log(k.scl)^2):sit log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m7
## m5
## m13 0.1523
## m2
## m0
## m4
## m6
## m1
## m9 -0.1649
## m3
## m12 -0.3441 -1.5620
## m11 -0.3265
## m10 -0.1797 -0.3511
## m15 -0.4872
## m8 +
## m14 + 0.1378 -2.0670
## m16 + -0.4079 -3.0940
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2
## m7
## m5
## m13
## m2
## m0
## m4
## m6
## m1
## m9
## m3
## m12 -0.1801 -1.0870
## m11 -0.1561
## m10
## m15 -0.3561
## m8
## m14
## m16 -0.5500 -0.2197
## log(k.scl):log(mu.scl):sit I(log(k.scl)^2):log(mu.scl):sit
## m7
## m5
## m13 +
## m2
## m0
## m4
## m6
## m1
## m9
## m3
## m12
## m11
## m10
## m15 +
## m8
## m14 + +
## m16 + +
## I(log(mu.scl)^2):log(k.scl):sit I(log(k.scl)^2):I(log(mu.scl)^2):sit
## m7
## m5
## m13
## m2
## m0
## m4
## m6
## m1
## m9
## m3
## m12
## m11
## m10
## m15 +
## m8
## m14
## m16 + +
## family init.theta df logLik AICc delta weight
## m7 NB(0.8795) 0.88 17 -747.270 1533.2 0.00 0.914
## m5 NB(0.7991) 0.799 12 -756.210 1538.7 5.50 0.058
## m13 NB(0.9276) 0.928 22 -744.781 1541.6 8.38 0.014
## m2 NB(0.7275) 0.728 8 -763.676 1544.4 11.17 0.003
## m0 NB(0.7134) 0.713 7 -765.115 1545.0 11.81 0.002
## m4 NB(0.7321) 0.732 9 -763.228 1545.8 12.54 0.002
## m6 NB(0.7667) 0.767 12 -759.864 1546.0 12.81 0.002
## m1 NB(0.717) 0.717 8 -764.729 1546.5 13.28 0.001
## m9 NB(0.7374) 0.737 10 -762.678 1547.0 13.74 0.001
## m3 NB(0.7234) 0.723 9 -764.108 1547.5 14.30 0.001
## m12 NB(0.8005) 0.8 15 -756.983 1547.6 14.36 0.001
## m11 NB(0.7464) 0.746 12 -761.859 1550.0 16.80 0.000
## m10 NB(0.7457) 0.746 12 -761.899 1550.1 16.88 0.000
## m15 NB(1.0533) 1.05 32 -734.328 1550.9 17.65 0.000
## m8 NB(0.8012) 0.801 17 -756.709 1552.1 18.88 0.000
## m14 NB(1.0251) 1.03 32 -737.850 1557.9 24.70 0.000
## m16 NB(1.3407) 1.34 47 -718.176 1575.0 41.81 0.000
## Abbreviations:
## family: NB(0.7134) = 'Negative Binomial(0.7134)',
## NB(0.717) = 'Negative Binomial(0.717)',
## NB(0.7234) = 'Negative Binomial(0.7234)',
## NB(0.7275) = 'Negative Binomial(0.7275)',
## NB(0.7321) = 'Negative Binomial(0.7321)',
## NB(0.7374) = 'Negative Binomial(0.7374)',
## NB(0.7457) = 'Negative Binomial(0.7457)',
## NB(0.7464) = 'Negative Binomial(0.7464)',
## NB(0.7667) = 'Negative Binomial(0.7667)',
## NB(0.7991) = 'Negative Binomial(0.7991)',
## NB(0.8005) = 'Negative Binomial(0.8005)',
## NB(0.8012) = 'Negative Binomial(0.8012)',
## NB(0.8795) = 'Negative Binomial(0.8795)',
## NB(0.9276) = 'Negative Binomial(0.9276)',
## NB(1.0251) = 'Negative Binomial(1.0251)',
## NB(1.0533) = 'Negative Binomial(1.0533)',
## NB(1.3407) = 'Negative Binomial(1.3407)'
## Models ranked by AICc(x)
aic.lmx(round(nococr140data$filter.feeder_bio*100), family=negative.binomial(theta = 1.3407), nococr140data)#m7
## Model selection table
## (Int) log(mxv) sit log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m7 -4.455 1.3450 + 0.746200 -1.05200
## m13 -8.321 1.8540 + 1.272000 1.5120
## m15 -7.535 1.8140 + 1.052000 1.6090 -1.02100
## m5 -4.607 1.2670 + 1.087000
## m14 -9.901 2.1120 + 1.933000 1.5090 0.01242
## m12 -4.772 1.2640 + 0.479300 0.4165 0.23060 0.47230
## m6 -4.174 1.1680 + 1.2170
## m8 -5.639 1.4690 + 1.2920 -0.98050
## m2 -3.329 1.0420 + 0.3066
## m16 -8.986 2.0390 + 2.073000 1.7230 -1.14000 0.33050
## m4 -3.288 1.0560 + 0.3099 -0.40090
## m9 -3.477 1.0640 + 0.100100 0.2767
## m11 -3.054 1.0030 + 0.005052 0.3702 -0.08885
## m0 -2.415 0.8971 +
## m10 -3.623 1.1130 + 0.187900 0.2757 -0.45820
## m3 -1.963 0.8296 + -0.023600 -0.10080
## m1 -2.381 0.8877 + 0.086180
## log(mu.scl):sit log(k.scl):sit I(log(mu.scl)^2):sit
## m7 + +
## m13 + +
## m15 + + +
## m5 +
## m14 + +
## m12
## m6 +
## m8 +
## m2
## m16 + + +
## m4
## m9
## m11
## m0
## m10
## m3
## m1
## I(log(k.scl)^2):sit log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m7
## m13 0.1278
## m15 -0.4765
## m5
## m14 + 0.1295 -2.0270
## m12 -0.3459 -1.5480
## m6
## m8 +
## m2
## m16 + -0.4079 -3.0940
## m4
## m9 -0.1650
## m11 -0.3276
## m0
## m10 -0.1797 -0.3518
## m3
## m1
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2
## m7
## m13
## m15 -0.3833
## m5
## m14
## m12 -0.1799 -1.0720
## m6
## m8
## m2
## m16 -0.5499 -0.2197
## m4
## m9
## m11 -0.1566
## m0
## m10
## m3
## m1
## log(k.scl):log(mu.scl):sit I(log(k.scl)^2):log(mu.scl):sit
## m7
## m13 +
## m15 +
## m5
## m14 + +
## m12
## m6
## m8
## m2
## m16 + +
## m4
## m9
## m11
## m0
## m10
## m3
## m1
## I(log(mu.scl)^2):log(k.scl):sit I(log(k.scl)^2):I(log(mu.scl)^2):sit
## m7
## m13
## m15 +
## m5
## m14
## m12
## m6
## m8
## m2
## m16 + +
## m4
## m9
## m11
## m0
## m10
## m3
## m1
## df logLik AICc delta weight
## m7 16 -753.517 1543.2 0.00 0.903
## m13 21 -749.367 1548.0 4.85 0.080
## m15 31 -736.186 1551.3 8.17 0.015
## m5 11 -765.902 1555.7 12.58 0.002
## m14 31 -740.154 1559.3 16.11 0.000
## m12 14 -766.425 1564.0 20.83 0.000
## m6 11 -771.300 1566.5 23.37 0.000
## m8 16 -766.165 1568.5 25.30 0.000
## m2 7 -777.769 1570.3 27.18 0.000
## m16 46 -718.176 1570.7 27.59 0.000
## m4 8 -776.985 1571.0 27.84 0.000
## m9 9 -776.061 1571.4 28.26 0.000
## m11 11 -774.584 1573.1 29.94 0.000
## m0 6 -780.289 1573.2 30.01 0.000
## m10 11 -774.684 1573.3 30.14 0.000
## m3 8 -778.506 1574.0 30.89 0.000
## m1 7 -779.631 1574.1 30.90 0.000
## Models ranked by AICc(x)
bestff<-glm(round(filter.feeder_bio*100)~log(maxvol)+site*(log(mu.scalar)+I(log(mu.scalar)^2)), data = nococr140data)
betterff<-glm(round(filter.feeder_bio*100)~log(maxvol)+site*log(mu.scalar)+I(log(mu.scalar)^2), data = nococr140data)
#looks like the effects of mu are entirely driven by cardoso...
nococr140data$resid.ff<-resid(glm.nb(round(filter.feeder_bio*100)~log(maxvol)+site, data=nococr140data, na.action=na.exclude))
aic.percent$draintrue[aic.percent$response=="Filter feeder"]<-Dsquared(glm(resid.ff~site*(log(mu.scalar)+I(log(mu.scalar)^2)),family=gaussian, nococr140data), adjust=TRUE) #0.1385931
aic.percent$drainfalse[aic.percent$response=="Filter feeder"]<-Dsquared(glm(resid.ff~site*(log(mu.scalar)+I(log(mu.scalar)^2)),family=gaussian, nococr140data), adjust=FALSE) #0.1385931
aic.percent$raintype[aic.percent$response=="Filter feeder"]<-"contingent"
#scraper - rain --------------
aic.lmxnb(round(fulldata$scraper_bio*10), fulldata)#m8 = site x k+k2
## Warning in glm.nb(y ~ log(maxvol) + site * log(mu.scalar) *
## log(k.scalar), : alternation limit reached
## Model selection table
## (Int) log(mxv) sit log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m8 -3.801 1.0840 + 0.7997 1.0850
## m6 -4.658 1.2970 + 0.8100
## m2 -3.663 1.1380 + 0.1853
## m0 -3.180 1.0620 +
## m4 -3.653 1.1200 + 0.1856 0.2484
## m1 -3.220 1.0690 + 0.009076
## m9 -3.690 1.1430 + 0.009830 0.2154
## m3 -3.217 1.0640 + 0.042810 0.03090
## m10 -3.590 1.1110 + 0.108500 0.2171 0.1922
## m11 -3.706 1.1410 + 0.040150 0.1930 0.02794
## m5 -3.239 1.0750 + 0.070990
## m12 -3.588 1.1070 + 0.132500 0.1931 0.02062 0.1679
## m13 -4.078 1.2000 + 0.095980 0.8142
## m7 -2.819 1.0050 + 0.043150 -0.01889
## m14 -3.398 1.0080 + 0.861400 0.8012 1.1510
## m15 -3.879 1.1730 + 0.025720 0.7220 -0.05454
## m16 -2.856 0.9588 + 0.468600 0.6763 -0.38680 0.6545
## log(mu.scl):sit log(k.scl):sit I(log(mu.scl)^2):sit
## m8 +
## m6 +
## m2
## m0
## m4
## m1
## m9
## m3
## m10
## m11
## m5 +
## m12
## m13 + +
## m7 + +
## m14 + +
## m15 + + +
## m16 + + +
## I(log(k.scl)^2):sit log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m8 +
## m6
## m2
## m0
## m4
## m1
## m9 0.097920
## m3
## m10 0.102600 -0.3035
## m11 0.124200
## m5
## m12 0.129100 -0.2803
## m13 0.006869
## m7
## m14 + 0.008739 -1.8800
## m15 0.148800
## m16 + 0.170100 -1.1500
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2
## m8
## m6
## m2
## m0
## m4
## m1
## m9
## m3
## m10
## m11 0.02410
## m5
## m12 0.02524 0.02668
## m13
## m7
## m14
## m15 0.11400
## m16 0.14420 0.74710
## log(k.scl):log(mu.scl):sit I(log(k.scl)^2):log(mu.scl):sit
## m8
## m6
## m2
## m0
## m4
## m1
## m9
## m3
## m10
## m11
## m5
## m12
## m13 +
## m7
## m14 + +
## m15 +
## m16 + +
## I(log(mu.scl)^2):log(k.scl):sit I(log(k.scl)^2):I(log(mu.scl)^2):sit
## m8
## m6
## m2
## m0
## m4
## m1
## m9
## m3
## m10
## m11
## m5
## m12
## m13
## m7
## m14
## m15 +
## m16 + +
## family init.theta df logLik AICc delta weight
## m8 NB(1.834) 1.83 23 -929.168 1910.3 0.00 0.924
## m6 NB(1.6317) 1.63 16 -941.054 1916.9 6.66 0.033
## m2 NB(1.5158) 1.52 10 -948.680 1918.5 8.20 0.015
## m0 NB(1.4955) 1.5 9 -950.188 1919.3 9.00 0.010
## m4 NB(1.5199) 1.52 11 -948.247 1919.8 9.56 0.008
## m1 NB(1.4957) 1.5 10 -950.175 1921.5 11.18 0.003
## m9 NB(1.5233) 1.52 12 -948.128 1921.8 11.57 0.003
## m3 NB(1.4988) 1.5 11 -950.011 1923.4 13.09 0.001
## m10 NB(1.5388) 1.54 14 -946.880 1923.9 13.64 0.001
## m11 NB(1.5264) 1.53 14 -947.954 1926.1 15.79 0.000
## m5 NB(1.5269) 1.53 16 -947.860 1930.5 20.27 0.000
## m12 NB(1.5422) 1.54 17 -946.684 1930.6 20.28 0.000
## m13 NB(1.7851) 1.79 30 -932.388 1935.2 24.90 0.000
## m7 NB(1.5889) 1.59 23 -943.772 1939.5 29.21 0.000
## m14 NB(2.1084) 2.11 44 -915.038 1942.1 31.80 0.000
## m15 NB(1.8715) 1.87 44 -927.661 1967.3 57.05 0.000
## m16 NB(2.387) 2.39 65 -902.448 1994.5 84.21 0.000
## Abbreviations:
## family: NB(1.4955) = 'Negative Binomial(1.4955)',
## NB(1.4957) = 'Negative Binomial(1.4957)',
## NB(1.4988) = 'Negative Binomial(1.4988)',
## NB(1.5158) = 'Negative Binomial(1.5158)',
## NB(1.5199) = 'Negative Binomial(1.5199)',
## NB(1.5233) = 'Negative Binomial(1.5233)',
## NB(1.5264) = 'Negative Binomial(1.5264)',
## NB(1.5269) = 'Negative Binomial(1.5269)',
## NB(1.5388) = 'Negative Binomial(1.5388)',
## NB(1.5422) = 'Negative Binomial(1.5422)',
## NB(1.5889) = 'Negative Binomial(1.5889)',
## NB(1.6317) = 'Negative Binomial(1.6317)',
## NB(1.7851) = 'Negative Binomial(1.7851)',
## NB(1.834) = 'Negative Binomial(1.834)',
## NB(1.8715) = 'Negative Binomial(1.8715)',
## NB(2.1084) = 'Negative Binomial(2.1084)',
## NB(2.387) = 'Negative Binomial(2.387)'
## Models ranked by AICc(x)
aic.lmx(round(fulldata$scraper_bio*10), family=negative.binomial(theta = 2.3887), fulldata)#m8
## Model selection table
## (Int) log(mxv) sit log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m8 -3.791 1.0820 + 0.7996 1.0930
## m6 -4.608 1.2880 + 0.8144
## m2 -3.640 1.1350 + 0.1813
## m4 -3.631 1.1180 + 0.1815 0.2256
## m0 -3.165 1.0600 +
## m14 -3.366 1.0030 + 0.852100 0.8006 1.1520
## m13 -4.000 1.1870 + 0.097190 0.8160
## m9 -3.657 1.1380 + 0.010220 0.2102
## m10 -3.553 1.1070 + 0.106300 0.2114 0.1705
## m1 -3.206 1.0670 + 0.009406
## m3 -3.203 1.0610 + 0.045630 0.03325
## m11 -3.671 1.1360 + 0.043240 0.1904 0.03044
## m12 -3.559 1.1030 + 0.137500 0.1902 0.02691 0.1567
## m5 -3.175 1.0640 + 0.069330
## m7 -2.776 0.9978 + 0.042220 -0.01867
## m15 -3.816 1.1620 + 0.026440 0.7228 -0.05550
## m16 -2.856 0.9588 + 0.468500 0.6763 -0.38680 0.6544
## log(mu.scl):sit log(k.scl):sit I(log(mu.scl)^2):sit
## m8 +
## m6 +
## m2
## m4
## m0
## m14 + +
## m13 + +
## m9
## m10
## m1
## m3
## m11
## m12
## m5 +
## m7 + +
## m15 + + +
## m16 + + +
## I(log(k.scl)^2):sit log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m8 +
## m6
## m2
## m4
## m0
## m14 + 0.0071020 -1.8600
## m13 -0.0005556
## m9 0.0957700
## m10 0.1001000 -0.2974
## m1
## m3
## m11 0.1186000
## m12 0.1234000 -0.2870
## m5
## m7
## m15 0.1425000
## m16 + 0.1701000 -1.1490
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2
## m8
## m6
## m2
## m4
## m0
## m14
## m13
## m9
## m10
## m1
## m3
## m11 0.02113
## m12 0.02238 0.01538
## m5
## m7
## m15 0.11500
## m16 0.14420 0.74720
## log(k.scl):log(mu.scl):sit I(log(k.scl)^2):log(mu.scl):sit
## m8
## m6
## m2
## m4
## m0
## m14 + +
## m13 +
## m9
## m10
## m1
## m3
## m11
## m12
## m5
## m7
## m15 +
## m16 + +
## I(log(mu.scl)^2):log(k.scl):sit I(log(k.scl)^2):I(log(mu.scl)^2):sit
## m8
## m6
## m2
## m4
## m0
## m14
## m13
## m9
## m10
## m1
## m3
## m11
## m12
## m5
## m7
## m15 +
## m16 + +
## df logLik AICc delta weight
## m8 22 -932.402 1914.2 0.00 0.999
## m6 15 -948.223 1928.9 14.70 0.001
## m2 9 -959.340 1937.6 23.36 0.000
## m4 10 -958.792 1938.7 24.47 0.000
## m0 8 -961.547 1939.8 25.60 0.000
## m14 43 -915.707 1940.2 25.99 0.000
## m13 29 -936.322 1940.3 26.10 0.000
## m9 11 -958.536 1940.4 26.19 0.000
## m10 13 -956.807 1941.5 27.26 0.000
## m1 9 -961.526 1942.0 27.74 0.000
## m3 10 -961.234 1943.6 29.36 0.000
## m11 13 -958.248 1944.4 30.14 0.000
## m12 16 -956.489 1947.8 33.58 0.000
## m5 15 -958.121 1948.7 34.50 0.000
## m7 22 -952.086 1953.6 39.37 0.000
## m15 43 -930.350 1969.5 55.28 0.000
## m16 64 -902.448 1990.3 76.06 0.000
## Models ranked by AICc(x)
bestsc<-glm.nb(round(scraper_bio*10)~log(maxvol)+site*(log(k.scalar)+I(log(k.scalar)^2)), data = fulldata)
#scrapers all go down as k does, curve mainly due to PR
Dsquared(bestsc, adjust=TRUE)#0.60
## [1] 0.6026472
fulldata$resid.scraper<-resid(glm.nb(round(scraper_bio*10)~log(maxvol)+site, data=fulldata, na.action=na.exclude))
aic.percent$draintrue[aic.percent$response=="Scraper"]<-Dsquared(glm(resid.scraper~site*(log(k.scalar)+I(log(k.scalar)^2)),family=gaussian, fulldata), adjust=TRUE) #0.09918389
aic.percent$drainfalse[aic.percent$response=="Scraper"]<-Dsquared(glm(resid.scraper~site*(log(k.scalar)+I(log(k.scalar)^2)),family=gaussian, fulldata), adjust=FALSE) #
aic.percent$raintype[aic.percent$response=="Scraper"]<-"contingent"
#gatherer - rain
aic.lmxnb(round(no67185data$gatherer_bio*10), no67185data)#no convergence with x 100, but with x 10 get m8 = site x k+k2
## Warning: glm.fit: algorithm did not converge
## Warning: alternation limit reached
## Model selection table
## (Int) log(mxv) sit log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m8 -1.6660 1.0980 + 0.47650 -3.6630
## m4 -1.8600 1.0080 + 0.22940 -0.5777
## m1 -1.3770 0.9068 + -0.1284
## m3 -1.3170 0.9024 + -0.2152 -0.08524
## m2 -1.6930 0.9662 + 0.22070
## m10 -2.0000 1.0070 + -0.3053 0.24190 -0.3859
## m0 -1.3710 0.9128 +
## m9 -1.7220 0.9634 + -0.1313 0.22940
## m12 -1.7650 0.9838 + -0.5717 0.15440 -0.26950 -0.8820
## m7 0.3442 0.7164 + -0.6346 -0.85930
## m11 -1.6110 0.9496 + -0.2277 0.13570 -0.09488
## m14 -2.1580 1.0970 + -1.0950 0.26020 -2.9060
## m5 -0.8605 0.8151 + -0.2059
## m6 -2.4210 1.0900 + 0.47050
## m13 -1.7740 0.9629 + -0.2702 0.19670
## m15 -0.5242 0.8628 + -0.7618 0.06018 -0.94520
## m16 -2.0110 1.2210 + -1.7100 0.29140 -1.40400 -4.5200
## log(mu.scl):sit log(k.scl):sit I(log(mu.scl)^2):sit
## m8 +
## m4
## m1
## m3
## m2
## m10
## m0
## m9
## m12
## m7 + +
## m11
## m14 + +
## m5 +
## m6 +
## m13 + +
## m15 + + +
## m16 + + +
## I(log(k.scl)^2):sit log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m8 +
## m4
## m1
## m3
## m2
## m10 0.02802 0.5559
## m0
## m9 0.02783
## m12 0.14150 1.1110
## m7
## m11 0.13000
## m14 + 0.48960 3.9810
## m5
## m6
## m13 0.68740
## m15 0.92220
## m16 + 0.54340 5.0750
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2
## m8
## m4
## m1
## m3
## m2
## m10
## m0
## m9
## m12 0.10230 0.5428
## m7
## m11 0.10350
## m14
## m5
## m6
## m13
## m15 0.16950
## m16 0.00363 2.7090
## log(k.scl):log(mu.scl):sit I(log(k.scl)^2):log(mu.scl):sit
## m8
## m4
## m1
## m3
## m2
## m10
## m0
## m9
## m12
## m7
## m11
## m14 + +
## m5
## m6
## m13 +
## m15 +
## m16 + +
## I(log(mu.scl)^2):log(k.scl):sit I(log(k.scl)^2):I(log(mu.scl)^2):sit
## m8
## m4
## m1
## m3
## m2
## m10
## m0
## m9
## m12
## m7
## m11
## m14
## m5
## m6
## m13
## m15 +
## m16 + +
## family init.theta df logLik AICc delta weight
## m8 NB(1.2911) 1.29 23 -902.112 1856.2 0.00 0.830
## m4 NB(1.0776) 1.08 11 -919.749 1862.8 6.62 0.030
## m1 NB(1.0655) 1.07 10 -920.972 1863.1 6.84 0.027
## m3 NB(1.0735) 1.07 11 -920.157 1863.7 7.44 0.020
## m2 NB(1.062) 1.06 10 -921.291 1863.7 7.48 0.020
## m10 NB(1.1081) 1.11 14 -916.770 1863.7 7.49 0.020
## m0 NB(1.0486) 1.05 9 -922.654 1864.2 7.99 0.015
## m9 NB(1.0801) 1.08 12 -919.516 1864.6 8.41 0.012
## m12 NB(1.1389) 1.14 17 -913.717 1864.7 8.43 0.012
## m7 NB(1.2127) 1.21 23 -907.000 1866.0 9.78 0.006
## m11 NB(1.0941) 1.09 14 -918.159 1866.5 10.27 0.005
## m14 NB(1.6297) 1.63 44 -878.659 1869.6 13.39 0.001
## m5 NB(1.0954) 1.1 16 -917.831 1870.5 14.29 0.001
## m6 NB(1.0888) 1.09 16 -918.819 1872.5 16.26 0.000
## m13 NB(1.1588) 1.16 30 -911.934 1894.4 38.15 0.000
## m15 NB(1.3359) 1.34 44 -896.915 1906.1 49.90 0.000
## m16 NB(2.0236) 2.02 65 -858.552 1907.5 51.30 0.000
## Abbreviations:
## family: NB(1.0486) = 'Negative Binomial(1.0486)',
## NB(1.062) = 'Negative Binomial(1.062)',
## NB(1.0655) = 'Negative Binomial(1.0655)',
## NB(1.0735) = 'Negative Binomial(1.0735)',
## NB(1.0776) = 'Negative Binomial(1.0776)',
## NB(1.0801) = 'Negative Binomial(1.0801)',
## NB(1.0888) = 'Negative Binomial(1.0888)',
## NB(1.0941) = 'Negative Binomial(1.0941)',
## NB(1.0954) = 'Negative Binomial(1.0954)',
## NB(1.1081) = 'Negative Binomial(1.1081)',
## NB(1.1389) = 'Negative Binomial(1.1389)',
## NB(1.1588) = 'Negative Binomial(1.1588)',
## NB(1.2127) = 'Negative Binomial(1.2127)',
## NB(1.2911) = 'Negative Binomial(1.2911)',
## NB(1.3359) = 'Negative Binomial(1.3359)',
## NB(1.6297) = 'Negative Binomial(1.6297)',
## NB(2.0236) = 'Negative Binomial(2.0236)'
## Models ranked by AICc(x)
aic.lmx(round(no67185data$gatherer_bio*10),family=negative.binomial(theta = 2.1662), no67185data)# m8 m14
## Warning: glm.fit: algorithm did not converge
## Model selection table
## (Int) log(mxv) sit log(mu.scl) log(k.scl) log(mu.scl)^2
## m14 -2.29700 1.1220 + -1.0780 0.25930
## m8 -1.94600 1.1450 + 0.47750
## m7 0.03333 0.7671 + -0.6299 -0.84030
## m16 -2.03700 1.2250 + -1.7130 0.28590 -1.40300
## m12 -1.92300 1.0110 + -0.5662 0.14900 -0.26070
## m10 -2.21100 1.0440 + -0.3018 0.23900
## m4 -2.12100 1.0540 + 0.22780
## m11 -1.88500 0.9970 + -0.2275 0.13310 -0.09380
## m9 -2.01700 1.0140 + -0.1305 0.22840
## m3 -1.60700 0.9523 + -0.2143 -0.08455
## m1 -1.67700 0.9584 + -0.1272
## m2 -1.99400 1.0180 + 0.21920
## m5 -1.19900 0.8737 + -0.1953
## m0 -1.67600 0.9651 +
## m6 -2.75800 1.1470 + 0.46490
## m15 -0.90400 0.9240 + -0.7595 0.05726 -0.91930
## m13 -2.17100 1.0320 + -0.2534 0.23980
## log(k.scl)^2 log(mu.scl):sit log(k.scl):sit I(log(mu.scl)^2):sit
## m14 -2.9170 + +
## m8 -3.6490 +
## m7 + +
## m16 -4.5210 + + +
## m12 -0.8526
## m10 -0.3830
## m4 -0.5806
## m11
## m9
## m3
## m1
## m2
## m5 +
## m0
## m6 +
## m15 + + +
## m13 + +
## I(log(k.scl)^2):sit log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m14 + 0.48890 3.9420
## m8 +
## m7
## m16 + 0.54610 5.0850
## m12 0.14110 1.0950
## m10 0.02625 0.5516
## m4
## m11 0.13260
## m9 0.02643
## m3
## m1
## m2
## m5
## m0
## m6
## m15 0.92280
## m13 0.63020
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2
## m14
## m8
## m7
## m16 0.008682 2.7140
## m12 0.103500 0.5214
## m10
## m4
## m11 0.105000
## m9
## m3
## m1
## m2
## m5
## m0
## m6
## m15 0.203000
## m13
## log(k.scl):log(mu.scl):sit I(log(k.scl)^2):log(mu.scl):sit
## m14 + +
## m8
## m7
## m16 + +
## m12
## m10
## m4
## m11
## m9
## m3
## m1
## m2
## m5
## m0
## m6
## m15 +
## m13 +
## I(log(mu.scl)^2):log(k.scl):sit I(log(k.scl)^2):I(log(mu.scl)^2):sit
## m14
## m8
## m7
## m16 + +
## m12
## m10
## m4
## m11
## m9
## m3
## m1
## m2
## m5
## m0
## m6
## m15 +
## m13
## df logLik AICc delta weight
## m14 43 -882.265 1873.6 0.00 0.966
## m8 22 -915.400 1880.3 6.67 0.034
## m7 22 -924.758 1899.0 25.38 0.000
## m16 64 -858.734 1903.7 30.05 0.000
## m12 16 -936.134 1907.1 33.51 0.000
## m10 13 -941.478 1910.8 37.23 0.000
## m4 10 -947.022 1915.2 41.56 0.000
## m11 13 -944.129 1916.1 42.53 0.000
## m9 11 -946.657 1916.7 43.06 0.000
## m3 10 -947.879 1916.9 43.27 0.000
## m1 9 -949.413 1917.7 44.13 0.000
## m2 9 -950.023 1919.0 45.35 0.000
## m5 15 -943.774 1920.0 46.45 0.000
## m0 8 -952.622 1922.0 48.37 0.000
## m6 15 -945.179 1922.9 49.26 0.000
## m15 43 -908.595 1926.3 52.66 0.000
## m13 29 -933.125 1934.0 60.42 0.000
## Models ranked by AICc(x)
bestga<-glm.nb(round(gatherer_bio*10)~log(maxvol)+site*(log(k.scalar)+I(log(k.scalar)^2)), data = no67185data)
#conting only in k2; for each site same direction effect of k2
betterga<-glm.nb(round(gatherer_bio*10)~log(maxvol)+site*(I(log(k.scalar)^2)), data = no67185data)
no67185data$resid.ga<-resid(glm.nb(round(gatherer_bio*10)~log(maxvol)+site, data=no67185data, na.action=na.exclude))
aic.percent$draintrue[aic.percent$response=="Gatherer"]<-Dsquared(glm(resid.ga~site*(log(k.scalar)+I(log(k.scalar)^2)),family=gaussian, no67185data), adjust=TRUE) #0.06993203
aic.percent$drainfalse[aic.percent$response=="Gatherer"]<-Dsquared(glm(resid.ga~site*(log(k.scalar)+I(log(k.scalar)^2)),family=gaussian, no67185data), adjust=FALSE) #0.06993203
aic.percent$raintype[aic.percent$response=="Gatherer"]<-"contingent"
#engulfer - rain
aic.lmxnb(round(noargco13data$engulfer_bio*100), noargco13data)# m0 m3 m1...NO RAINFALL EFFECT
## Warning: glm.fit: algorithm did not converge
## Model selection table
## (Int) log(mxv) sit log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 5.069 0.1753 +
## m3 4.817 0.2332 + -0.31890 -0.2132
## m1 4.846 0.2014 + -0.09045
## m2 5.030 0.1810 + 0.01474
## m4 5.407 0.1333 + 0.01075 -0.14290
## m6 3.905 0.3435 + -0.01793
## m9 4.953 0.1864 + -0.09046 0.06434
## m11 4.905 0.2205 + -0.31930 0.06872 -0.2119
## m5 5.167 0.1461 + -0.27940
## m10 5.568 0.1080 + -0.03980 0.05566 -0.20440
## m7 4.802 0.2206 + -0.46390 -0.1751
## m12 5.344 0.1612 + -0.27050 0.06056 -0.1718 -0.06218
## m8 5.282 0.1389 + -0.02780 0.12730
## m13 3.409 0.3999 + -0.28140 -0.02603
## m15 4.454 0.2712 + -0.47830 -0.17920 -0.1796
## m14 5.289 0.0995 + -0.56950 -0.03970 0.55430
## m16 8.732 -0.3522 + -1.07900 -0.10500 -0.4240 0.10320
## log(mu.scl):sit log(k.scl):sit I(log(mu.scl)^2):sit
## m0
## m3
## m1
## m2
## m4
## m6 +
## m9
## m11
## m5 +
## m10
## m7 + +
## m12
## m8 +
## m13 + +
## m15 + + +
## m14 + +
## m16 + + +
## I(log(k.scl)^2):sit log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m3
## m1
## m2
## m4
## m6
## m9 0.115200
## m11 0.093180
## m5
## m10 0.115500 -0.1582
## m7
## m12 0.096500 -0.1367
## m8 +
## m13 -0.003995
## m15 0.172800
## m14 + -0.014740 0.8120
## m16 + 0.064510 1.6370
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2
## m0
## m3
## m1
## m2
## m4
## m6
## m9
## m11 -0.004946
## m5
## m10
## m7
## m12 -0.001868 -0.1257
## m8
## m13
## m15 0.154000
## m14
## m16 0.054900 0.6502
## log(k.scl):log(mu.scl):sit I(log(k.scl)^2):log(mu.scl):sit
## m0
## m3
## m1
## m2
## m4
## m6
## m9
## m11
## m5
## m10
## m7
## m12
## m8
## m13 +
## m15 +
## m14 + +
## m16 + +
## I(log(mu.scl)^2):log(k.scl):sit I(log(k.scl)^2):I(log(mu.scl)^2):sit
## m0
## m3
## m1
## m2
## m4
## m6
## m9
## m11
## m5
## m10
## m7
## m12
## m8
## m13
## m15 +
## m14
## m16 + +
## family init.theta df logLik AICc delta weight
## m0 NB(0.452) 0.452 7 -733.802 1482.4 0.00 0.377
## m3 NB(0.4636) 0.464 9 -732.032 1483.4 0.97 0.232
## m1 NB(0.4535) 0.454 8 -733.563 1484.2 1.76 0.156
## m2 NB(0.4521) 0.452 8 -733.800 1484.6 2.23 0.123
## m4 NB(0.4523) 0.452 9 -733.769 1486.9 4.44 0.041
## m6 NB(0.4745) 0.474 12 -730.403 1487.1 4.72 0.036
## m9 NB(0.4544) 0.454 10 -733.435 1488.5 6.08 0.018
## m11 NB(0.4644) 0.464 12 -731.913 1490.2 7.74 0.008
## m5 NB(0.4624) 0.462 12 -732.190 1490.7 8.30 0.006
## m10 NB(0.4548) 0.455 12 -733.361 1493.1 10.64 0.002
## m7 NB(0.4879) 0.488 17 -728.309 1495.4 12.95 0.001
## m12 NB(0.4649) 0.465 15 -731.834 1497.3 14.92 0.000
## m8 NB(0.4817) 0.482 17 -729.373 1497.5 15.08 0.000
## m13 NB(0.4927) 0.493 22 -727.748 1507.7 25.25 0.000
## m15 NB(0.5541) 0.554 32 -719.032 1520.6 38.18 0.000
## m14 NB(0.5255) 0.525 32 -723.045 1528.6 46.21 0.000
## m16 NB(0.6766) 0.677 47 -702.746 1545.1 62.66 0.000
## Abbreviations:
## family: NB(0.452) = 'Negative Binomial(0.452)',
## NB(0.4521) = 'Negative Binomial(0.4521)',
## NB(0.4523) = 'Negative Binomial(0.4523)',
## NB(0.4535) = 'Negative Binomial(0.4535)',
## NB(0.4544) = 'Negative Binomial(0.4544)',
## NB(0.4548) = 'Negative Binomial(0.4548)',
## NB(0.4624) = 'Negative Binomial(0.4624)',
## NB(0.4636) = 'Negative Binomial(0.4636)',
## NB(0.4644) = 'Negative Binomial(0.4644)',
## NB(0.4649) = 'Negative Binomial(0.4649)',
## NB(0.4745) = 'Negative Binomial(0.4745)',
## NB(0.4817) = 'Negative Binomial(0.4817)',
## NB(0.4879) = 'Negative Binomial(0.4879)',
## NB(0.4927) = 'Negative Binomial(0.4927)',
## NB(0.5255) = 'Negative Binomial(0.5255)',
## NB(0.5541) = 'Negative Binomial(0.5541)',
## NB(0.6766) = 'Negative Binomial(0.6766)'
## Models ranked by AICc(x)
aic.lmx(round(noargco13data$engulfer_bio*100), family=negative.binomial(theta = 0.6766), noargco13data)#m3 m0
## Warning: glm.fit: algorithm did not converge
## Model selection table
## (Int) log(mxv) sit log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m3 4.849 0.22860 + -0.31940 -0.2137
## m0 5.095 0.17160 +
## m6 3.931 0.33980 + -0.01809
## m1 4.879 0.19660 + -0.09009
## m2 5.054 0.17750 + 0.01561
## m4 5.431 0.12980 + 0.01166 -0.14370
## m9 4.980 0.18250 + -0.09023 0.06504
## m11 4.929 0.21710 + -0.32010 0.06893 -0.2125
## m5 5.179 0.14430 + -0.27940
## m7 4.786 0.22290 + -0.46390 -0.1751
## m10 5.587 0.10540 + -0.04025 0.05657 -0.20400
## m8 5.305 0.13560 + -0.02792 0.12660
## m12 5.361 0.15890 + -0.27320 0.06108 -0.1736 -0.06454
## m13 3.438 0.39560 + -0.28130 -0.02620
## m15 4.394 0.28000 + -0.47840 -0.17980 -0.1797
## m14 5.305 0.09732 + -0.56940 -0.03976 0.55390
## m16 8.733 -0.35240 + -1.07900 -0.10500 -0.4240 0.10330
## log(mu.scl):sit log(k.scl):sit I(log(mu.scl)^2):sit
## m3
## m0
## m6 +
## m1
## m2
## m4
## m9
## m11
## m5 +
## m7 + +
## m10
## m8 +
## m12
## m13 + +
## m15 + + +
## m14 + +
## m16 + + +
## I(log(k.scl)^2):sit log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m3
## m0
## m6
## m1
## m2
## m4
## m9 0.115100
## m11 0.094270
## m5
## m7
## m10 0.115500 -0.1561
## m8 +
## m12 0.097520 -0.1313
## m13 -0.004014
## m15 0.173800
## m14 + -0.014730 0.8119
## m16 + 0.064500 1.6370
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2
## m3
## m0
## m6
## m1
## m2
## m4
## m9
## m11 -0.004133
## m5
## m7
## m10
## m8
## m12 -0.001123 -0.1219
## m13
## m15 0.155100
## m14
## m16 0.054890 0.6502
## log(k.scl):log(mu.scl):sit I(log(k.scl)^2):log(mu.scl):sit
## m3
## m0
## m6
## m1
## m2
## m4
## m9
## m11
## m5
## m7
## m10
## m8
## m12
## m13 +
## m15 +
## m14 + +
## m16 + +
## I(log(mu.scl)^2):log(k.scl):sit I(log(k.scl)^2):I(log(mu.scl)^2):sit
## m3
## m0
## m6
## m1
## m2
## m4
## m9
## m11
## m5
## m7
## m10
## m8
## m12
## m13
## m15 +
## m14
## m16 + +
## df logLik AICc delta weight
## m3 8 -737.338 1491.7 0.00 0.364
## m0 6 -739.948 1492.5 0.78 0.247
## m6 11 -735.008 1494.0 2.25 0.118
## m1 7 -739.596 1494.0 2.28 0.116
## m2 7 -739.945 1494.7 2.98 0.082
## m4 8 -739.898 1496.8 5.12 0.028
## m9 9 -739.406 1498.1 6.41 0.015
## m11 11 -737.163 1498.3 6.56 0.014
## m5 11 -737.583 1499.1 7.40 0.009
## m7 16 -732.166 1500.5 8.80 0.004
## m10 11 -739.297 1502.5 10.83 0.002
## m8 16 -733.552 1503.3 11.57 0.001
## m12 14 -737.052 1505.3 13.57 0.000
## m13 21 -731.343 1512.1 20.36 0.000
## m15 31 -720.373 1520.0 28.28 0.000
## m14 31 -725.246 1529.7 38.02 0.000
## m16 46 -702.746 1540.7 49.01 0.000
## Models ranked by AICc(x)
noargco13data$resid.en<-resid(glm.nb(round(noargco13data$engulfer_bio*100)~log(maxvol)+site, data=noargco13data, na.action=na.exclude))
aic.percent$draintrue[aic.percent$response=="Engulfer"]<-Dsquared(glm(resid.en~(log(mu.scalar)+I(log(mu.scalar)^2)),family=gaussian, noargco13data), adjust=TRUE) #0
aic.percent$drainfalse[aic.percent$response=="Engulfer"]<-Dsquared(glm(resid.en~(log(mu.scalar)+I(log(mu.scalar)^2)),family=gaussian, noargco13data), adjust=FALSE)
aic.percent$raintype[aic.percent$response=="Engulfer"]<-"ns"
#piercer - rain -----------
aic.lmxnb(round(nococrprdata$piercer_bio*10), nococrprdata) #m6 m1 m2 m0...only one where sig conting, but m0 in top set
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning: glm.fit: fitted rates numerically 0 occurred
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning: glm.fit: fitted rates numerically 0 occurred
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning: glm.fit: fitted rates numerically 0 occurred
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning: glm.fit: fitted rates numerically 0 occurred
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning: glm.fit: fitted rates numerically 0 occurred
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning: glm.fit: fitted rates numerically 0 occurred
## Warning in glm.nb(y ~ log(maxvol) + site + log(k.scalar) * (log(mu.scalar)
## + : alternation limit reached
## Warning: glm.fit: fitted rates numerically 0 occurred
## Warning in glm.nb(y ~ log(maxvol) + site + (log(k.scalar) +
## I(log(k.scalar)^2)) * : alternation limit reached
## Warning: glm.fit: fitted rates numerically 0 occurred
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning: glm.fit: fitted rates numerically 0 occurred
## Warning in glm.nb(y ~ log(maxvol) + site * log(k.scalar) * (log(mu.scalar)
## + : alternation limit reached
## Warning in glm.nb(y ~ log(maxvol) + site * (log(k.scalar) +
## I(log(k.scalar)^2)) * : alternation limit reached
## Model selection table
## (Int) log(mxv) sit log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m2 -40.66 -0.3646 + -28.710000
## m4 -42.02 -0.3646 + -15.350000 22.09000
## m0 -21.47 -0.3180 +
## m9 -39.52 -0.5613 + -0.01460 -28.740000
## m1 -21.49 -0.3108 + 0.12570
## m3 -10.93 -2.0180 + -0.49360 -1.390000
## m6 -21.20 -0.3646 + -0.009348
## m11 -76.94 2.7160 + 0.02878 -42.710000 -0.057520
## m10 -40.89 -0.5613 + -0.01216 -15.380000 22.10000
## m5 -21.52 -0.3108 + -0.02181
## m12 -79.05 2.7160 + 0.04313 -22.680000 -0.029220 32.52000
## m8 -21.21 -0.3646 + -0.009112 0.01479
## m7 -11.79 -2.0180 + -0.14690 -0.007612
## m13 -20.08 -0.5613 + -0.03982 -0.021540
## m15 -52.70 2.7160 + 0.23060 0.511400 0.039600
## m14 -20.09 -0.5613 + -0.05798 -0.021000 0.03260
## m16 -52.61 2.7160 + 0.31950 0.375700 0.032830 -0.26170
## log(mu.scl):sit log(k.scl):sit I(log(mu.scl)^2):sit
## m2
## m4
## m0
## m9
## m1
## m3
## m6 +
## m11
## m10
## m5 +
## m12
## m8 +
## m7 + +
## m13 + +
## m15 + + +
## m14 + +
## m16 + + +
## I(log(k.scl)^2):sit log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m2
## m4
## m0
## m9 -0.22010
## m1
## m3
## m6
## m11 0.99970
## m10 -0.09781 0.17130
## m5
## m12 0.65660 -0.52480
## m8 +
## m7
## m13 -0.03468
## m15 -0.24960
## m14 + -0.03389 0.05664
## m16 + -0.07533 -0.22900
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2
## m2
## m4
## m0
## m9
## m1
## m3
## m6
## m11 1.9470
## m10
## m5
## m12 1.1540 -1.20300
## m8
## m7
## m13
## m15 -0.4033
## m14
## m16 -0.2786 0.03376
## log(k.scl):log(mu.scl):sit I(log(k.scl)^2):log(mu.scl):sit
## m2
## m4
## m0
## m9
## m1
## m3
## m6
## m11
## m10
## m5
## m12
## m8
## m7
## m13 +
## m15 +
## m14 + +
## m16 + +
## I(log(mu.scl)^2):log(k.scl):sit I(log(k.scl)^2):I(log(mu.scl)^2):sit
## m2
## m4
## m0
## m9
## m1
## m3
## m6
## m11
## m10
## m5
## m12
## m8
## m7
## m13
## m15 +
## m14
## m16 + +
## family init.theta df logLik AICc delta weight
## m2 NB(32488.04) 32500 7 -7.302 29.6 0.00 0.598
## m4 NB(32488.11) 32500 8 -7.302 31.9 2.30 0.189
## m0 NB(43775.7) 43800 6 -10.598 33.9 4.33 0.068
## m9 NB(28419.54) 28400 9 -7.274 34.2 4.58 0.061
## m1 NB(318888.7) 319000 7 -10.573 36.1 6.54 0.023
## m3 NB(18845.81) 18800 8 -9.549 36.4 6.79 0.020
## m6 NB(32489) 32500 10 -7.302 36.6 7.02 0.018
## m11 NB(0.879) 0.879 11 -6.160 36.8 7.16 0.017
## m10 NB(28419.47) 28400 11 -7.274 39.0 9.39 0.005
## m5 NB(317979) 318000 10 -10.573 43.2 13.56 0.001
## m12 NB(0.879) 0.879 14 -6.160 44.3 14.71 0.000
## m8 NB(32488.91) 32500 14 -7.302 46.6 17.00 0.000
## m7 NB(18845.7) 18800 14 -9.549 51.1 21.49 0.000
## m13 NB(28419.88) 28400 18 -7.274 57.3 27.72 0.000
## m15 NB(0.879) 0.879 26 -6.160 79.4 49.81 0.000
## m14 NB(28419.56) 28400 26 -7.274 81.6 52.04 0.000
## m16 NB(0.879) 0.879 38 -6.160 124.9 95.31 0.000
## Abbreviations:
## family: NB(0.879) = 'Negative Binomial(0.879)',
## NB(18845.7) = 'Negative Binomial(18845.7)',
## NB(18845.81) = 'Negative Binomial(18845.81)',
## NB(28419.47) = 'Negative Binomial(28419.47)',
## NB(28419.54) = 'Negative Binomial(28419.54)',
## NB(28419.56) = 'Negative Binomial(28419.56)',
## NB(28419.88) = 'Negative Binomial(28419.88)',
## NB(317979) = 'Negative Binomial(317979)',
## NB(318888.7) = 'Negative Binomial(318888.7)',
## NB(32488.04) = 'Negative Binomial(32488.04)',
## NB(32488.11) = 'Negative Binomial(32488.11)',
## NB(32488.91) = 'Negative Binomial(32488.91)',
## NB(32489) = 'Negative Binomial(32489)',
## NB(43775.7) = 'Negative Binomial(43775.7)'
## Models ranked by AICc(x)
aic.lmx(round(nococrprdata$piercer_bio*10), family=negative.binomial(theta = 0.5644), nococrprdata)#m6 = site x k
## Model selection table
## (Int) log(mxv) sit log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m2 -38.96 -0.4924 + -28.04000
## m4 -40.35 -0.4924 + -15.03000 21.61000
## m0 -19.70 -0.3639 +
## m9 -36.59 -0.9056 + -0.01654 -28.08000
## m1 -19.48 -0.3969 + 0.14160
## m3 -10.29 -2.0370 + -0.39640 -1.494000
## m6 -19.96 -0.4924 + -0.01200
## m11 -65.24 3.5400 + 0.03007 -30.83000 -0.042640
## m10 -37.97 -0.9056 + -0.02222 -15.07000 21.60000
## m5 -19.51 -0.3969 + -0.02782
## m12 -64.65 3.5400 + 0.05678 -15.80000 -0.039320 22.49000
## m8 -19.96 -0.4924 + -0.01200 0.01855
## m7 -11.18 -2.0370 + -0.14640 -0.006129
## m13 -17.61 -0.9056 + -0.06470 -0.03163
## m15 -44.58 3.5400 + 0.40840 0.67920 0.160100
## m14 -17.62 -0.9056 + -0.09278 -0.03138 0.04345
## m16 -43.36 3.5400 + 0.50780 0.67410 0.107600 -0.70280
## log(mu.scl):sit log(k.scl):sit I(log(mu.scl)^2):sit
## m2
## m4
## m0
## m9
## m1
## m3
## m6 +
## m11
## m10
## m5 +
## m12
## m8 +
## m7 + +
## m13 + +
## m15 + + +
## m14 + +
## m16 + + +
## I(log(k.scl)^2):sit log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m2
## m4
## m0
## m9 -0.29800
## m1
## m3
## m6
## m11 1.06300
## m10 -0.13180 0.25160
## m5
## m12 0.74940 -0.50850
## m8 +
## m7
## m13 -0.05279
## m15 -0.17060
## m14 + -0.05153 0.08805
## m16 + -0.17040 -0.28380
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2
## m2
## m4
## m0
## m9
## m1
## m3
## m6
## m11 2.1240
## m10
## m5
## m12 1.2790 -1.2270
## m8
## m7
## m13
## m15 -0.4472
## m14
## m16 -0.4434 0.1924
## log(k.scl):log(mu.scl):sit I(log(k.scl)^2):log(mu.scl):sit
## m2
## m4
## m0
## m9
## m1
## m3
## m6
## m11
## m10
## m5
## m12
## m8
## m7
## m13 +
## m15 +
## m14 + +
## m16 + +
## I(log(mu.scl)^2):log(k.scl):sit I(log(k.scl)^2):I(log(mu.scl)^2):sit
## m2
## m4
## m0
## m9
## m1
## m3
## m6
## m11
## m10
## m5
## m12
## m8
## m7
## m13
## m15 +
## m14
## m16 + +
## df logLik AICc delta weight
## m2 6 -6.968 26.7 0.00 0.564
## m4 7 -6.968 28.9 2.26 0.182
## m0 5 -9.830 30.2 3.51 0.098
## m9 8 -6.941 31.2 4.50 0.059
## m1 6 -9.805 32.4 5.68 0.033
## m3 7 -8.905 32.8 6.13 0.026
## m6 9 -6.968 33.6 6.89 0.018
## m11 10 -6.179 34.4 7.70 0.012
## m10 10 -6.941 35.9 9.22 0.006
## m5 9 -9.805 39.2 12.57 0.001
## m12 13 -6.179 41.8 15.11 0.000
## m8 13 -6.968 43.4 16.69 0.000
## m7 13 -8.905 47.2 20.57 0.000
## m13 17 -6.941 53.9 27.20 0.000
## m15 25 -6.179 76.2 49.51 0.000
## m14 25 -6.941 77.7 51.03 0.000
## m16 37 -6.179 120.6 93.97 0.000
## Models ranked by AICc(x)
bestpi<-glm.nb(round(piercer_bio*10)~log(maxvol)+site*log(k.scalar), data = nococrprdata)
## Warning: glm.fit: fitted rates numerically 0 occurred
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
Anova(m6, type=3) #sig site * logk effect p=0.049
## Error in Anova(m6, type = 3): object 'm6' not found
nococrprdata$resid.pi<-resid(glm.nb(round(nococrprdata$piercer_bio*10)~log(maxvol)+site, data=nococrprdata, na.action=na.exclude))
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
aic.percent$draintrue[aic.percent$response=="Piercer"]<-Dsquared(glm(resid.pi~site*log(k.scalar),family=gaussian, nococrprdata), adjust=TRUE) #0.06042827
aic.percent$drainfalse[aic.percent$response=="Piercer"]<-Dsquared(glm(resid.pi~site*log(k.scalar),family=gaussian, nococrprdata), adjust=FALSE)
aic.percent$raintype[aic.percent$response=="Piercer"]<-"contingent"
#bacteria - rain ---------------
aic.lmxnb(round(noargco123data$bacteria.per.nl.final*100), noargco123data) #m1 m5 ...mu or site x mu
## Model selection table
## (Int) log(mxv) sit log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m1 2.0670 0.5898 + -0.14170
## m5 1.3130 0.6931 + -0.26340
## m3 2.0160 0.5950 + -0.12200 0.01901
## m9 1.9460 0.6073 + -0.14040 0.03419
## m7 1.6880 0.6525 + -0.37180 -0.10800
## m0 2.0050 0.6078 +
## m12 0.9864 0.7182 + 0.14720 -0.05584 0.19180 0.55250
## m11 1.9090 0.6113 + -0.12580 -0.04567 0.01179
## m10 1.5990 0.6553 + -0.08181 0.03510 0.02475
## m2 1.8440 0.6309 + 0.04944
## m4 1.7070 0.6471 + 0.04932 0.07926
## m8 1.4730 0.6945 + 0.04725 -0.22240
## m6 2.1930 0.5807 + 0.05143
## m13 1.9300 0.6040 + -0.25970 -0.02907
## m14 0.7767 0.7906 + -0.08952 -0.03826 -0.50880
## m15 1.9280 0.6189 + -0.37940 -0.03874 -0.11820
## m16 1.7680 0.6451 + -0.06747 -0.03156 0.02351 -0.09999
## log(mu.scl):sit log(k.scl):sit I(log(mu.scl)^2):sit
## m1
## m5 +
## m3
## m9
## m7 + +
## m0
## m12
## m11
## m10
## m2
## m4
## m8 +
## m6 +
## m13 + +
## m14 + +
## m15 + + +
## m16 + + +
## I(log(k.scl)^2):sit log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m1
## m5
## m3
## m9 -0.008066
## m7
## m0
## m12 0.081070 -0.8066
## m11 0.076980
## m10 -0.001849 -0.2018
## m2
## m4
## m8 +
## m6
## m13 -0.130800
## m14 + -0.131800 -0.6177
## m15 -0.151200
## m16 + -0.186200 -1.1060
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2
## m1
## m5
## m3
## m9
## m7
## m0
## m12 0.103000 -0.5556
## m11 0.091070
## m10
## m2
## m4
## m8
## m6
## m13
## m14
## m15 0.001436
## m16 -0.014540 -0.5034
## log(k.scl):log(mu.scl):sit I(log(k.scl)^2):log(mu.scl):sit
## m1
## m5
## m3
## m9
## m7
## m0
## m12
## m11
## m10
## m2
## m4
## m8
## m6
## m13 +
## m14 + +
## m15 +
## m16 + +
## I(log(mu.scl)^2):log(k.scl):sit I(log(k.scl)^2):I(log(mu.scl)^2):sit
## m1
## m5
## m3
## m9
## m7
## m0
## m12
## m11
## m10
## m2
## m4
## m8
## m6
## m13
## m14
## m15 +
## m16 + +
## family init.theta df logLik AICc delta weight
## m1 NB(2.6407) 2.64 8 -1085.730 2188.5 0.00 0.477
## m5 NB(2.7703) 2.77 12 -1081.904 2190.2 1.66 0.208
## m3 NB(2.6431) 2.64 9 -1085.657 2190.7 2.13 0.164
## m9 NB(2.6434) 2.64 10 -1085.650 2193.0 4.43 0.052
## m7 NB(2.9334) 2.93 17 -1077.356 2193.6 5.04 0.038
## m0 NB(2.5054) 2.51 7 -1089.954 2194.7 6.20 0.021
## m12 NB(2.7928) 2.79 15 -1081.232 2196.2 7.69 0.010
## m11 NB(2.6625) 2.66 12 -1085.075 2196.5 8.00 0.009
## m10 NB(2.6602) 2.66 12 -1085.143 2196.7 8.14 0.008
## m2 NB(2.5095) 2.51 8 -1089.819 2196.7 8.18 0.008
## m4 NB(2.5113) 2.51 9 -1089.764 2198.9 10.34 0.003
## m8 NB(2.8148) 2.81 17 -1080.629 2200.1 11.59 0.001
## m6 NB(2.5256) 2.53 12 -1089.308 2205.0 16.47 0.000
## m13 NB(2.8362) 2.84 22 -1080.038 2212.4 23.91 0.000
## m14 NB(3.2891) 3.29 32 -1068.352 2219.7 31.20 0.000
## m15 NB(3.1061) 3.11 32 -1072.831 2228.7 40.16 0.000
## m16 NB(4.2431) 4.24 47 -1048.617 2238.2 49.71 0.000
## Abbreviations:
## family: NB(2.5054) = 'Negative Binomial(2.5054)',
## NB(2.5095) = 'Negative Binomial(2.5095)',
## NB(2.5113) = 'Negative Binomial(2.5113)',
## NB(2.5256) = 'Negative Binomial(2.5256)',
## NB(2.6407) = 'Negative Binomial(2.6407)',
## NB(2.6431) = 'Negative Binomial(2.6431)',
## NB(2.6434) = 'Negative Binomial(2.6434)',
## NB(2.6602) = 'Negative Binomial(2.6602)',
## NB(2.6625) = 'Negative Binomial(2.6625)',
## NB(2.7703) = 'Negative Binomial(2.7703)',
## NB(2.7928) = 'Negative Binomial(2.7928)',
## NB(2.8148) = 'Negative Binomial(2.8148)',
## NB(2.8362) = 'Negative Binomial(2.8362)',
## NB(2.9334) = 'Negative Binomial(2.9334)',
## NB(3.1061) = 'Negative Binomial(3.1061)',
## NB(3.2891) = 'Negative Binomial(3.2891)',
## NB(4.2431) = 'Negative Binomial(4.2431)'
## Models ranked by AICc(x)
aic.lmx(round(noargco123data$bacteria.per.nl.final*100), family=negative.binomial(theta = 4.2431), noargco123data) #m7 m5...both site x mu
## Model selection table
## (Int) log(mxv) sit log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m7 1.6940 0.6517 + -0.37170 -0.10800
## m5 1.3220 0.6918 + -0.26320
## m1 2.0750 0.5887 + -0.14170
## m3 2.0240 0.5939 + -0.12220 0.01891
## m12 0.9942 0.7171 + 0.14640 -0.05581 0.19120 0.55210
## m9 1.9540 0.6061 + -0.14050 0.03425
## m8 1.4810 0.6934 + 0.04742 -0.22250
## m11 1.9180 0.6100 + -0.12590 -0.04569 0.01169
## m10 1.6060 0.6543 + -0.08189 0.03516 0.02598
## m0 2.0150 0.6064 +
## m2 1.8540 0.6294 + 0.04937
## m14 0.7835 0.7896 + -0.08953 -0.03803 -0.50850
## m4 1.7150 0.6460 + 0.04924 0.08061
## m13 1.9400 0.6026 + -0.25950 -0.02872
## m6 2.2040 0.5791 + 0.05162
## m15 1.9330 0.6182 + -0.37930 -0.03857 -0.11820
## m16 1.7670 0.6452 + -0.06747 -0.03157 0.02351 -0.10000
## log(mu.scl):sit log(k.scl):sit I(log(mu.scl)^2):sit
## m7 + +
## m5 +
## m1
## m3
## m12
## m9
## m8 +
## m11
## m10
## m0
## m2
## m14 + +
## m4
## m13 + +
## m6 +
## m15 + + +
## m16 + + +
## I(log(k.scl)^2):sit log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m7
## m5
## m1
## m3
## m12 0.081400 -0.8047
## m9 -0.007814
## m8 +
## m11 0.077310
## m10 -0.001606 -0.2015
## m0
## m2
## m14 + -0.131700 -0.6171
## m4
## m13 -0.130600
## m6
## m15 -0.150900
## m16 + -0.186200 -1.1060
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2
## m7
## m5
## m1
## m3
## m12 0.103000 -0.5541
## m9
## m8
## m11 0.091110
## m10
## m0
## m2
## m14
## m4
## m13
## m6
## m15 0.001566
## m16 -0.014530 -0.5034
## log(k.scl):log(mu.scl):sit I(log(k.scl)^2):log(mu.scl):sit
## m7
## m5
## m1
## m3
## m12
## m9
## m8
## m11
## m10
## m0
## m2
## m14 + +
## m4
## m13 +
## m6
## m15 +
## m16 + +
## I(log(mu.scl)^2):log(k.scl):sit I(log(k.scl)^2):I(log(mu.scl)^2):sit
## m7
## m5
## m1
## m3
## m12
## m9
## m8
## m11
## m10
## m0
## m2
## m14
## m4
## m13
## m6
## m15 +
## m16 + +
## df logLik AICc delta weight
## m7 16 -1083.351 2203.0 0.00 0.555
## m5 11 -1090.099 2204.2 1.21 0.303
## m1 7 -1096.084 2207.0 4.01 0.075
## m3 8 -1095.967 2209.0 6.02 0.027
## m12 14 -1089.099 2209.5 6.47 0.022
## m9 9 -1095.956 2211.3 8.27 0.009
## m8 16 -1088.173 2212.6 9.64 0.004
## m11 11 -1095.038 2214.1 11.09 0.002
## m10 11 -1095.146 2214.3 11.31 0.002
## m0 6 -1103.036 2218.7 15.70 0.000
## m2 7 -1102.810 2220.4 17.46 0.000
## m14 31 -1071.070 2221.9 18.87 0.000
## m4 8 -1102.714 2222.5 19.51 0.000
## m13 21 -1087.278 2224.1 21.15 0.000
## m6 11 -1101.947 2227.9 24.91 0.000
## m15 31 -1077.009 2233.7 30.75 0.000
## m16 46 -1048.617 2233.8 30.83 0.000
## Models ranked by AICc(x)
m5<-glm.nb(round(bacteria.per.nl.final*100)~log(maxvol)+site*log(mu.scalar), data = noargco123data)
m1<-glm.nb(round(bacteria.per.nl.final*100)~log(maxvol)+site+log(mu.scalar), data = noargco123data)
anova(m1,m5, test="Chisq")#not sig different fit, and site:log mu not sig in m5, so m1 best
## Likelihood ratio tests of Negative Binomial Models
##
## Response: round(bacteria.per.nl.final * 100)
## Model theta Resid. df 2 x log-lik.
## 1 log(maxvol) + site + log(mu.scalar) 2.640749 137 -2171.461
## 2 log(maxvol) + site * log(mu.scalar) 2.770277 133 -2163.808
## Test df LR stat. Pr(Chi)
## 1
## 2 1 vs 2 4 7.652818 0.1051564
bestba<-glm.nb(round(bacteria.per.nl.final*100)~log(maxvol)+site+log(mu.scalar), data = noargco123data)
visreg(bestba, "mu.scalar", by = "site")
noargco123data$resid.ba<-resid(glm.nb(round(bacteria.per.nl.final*100)~log(maxvol)+site, data=noargco123data, na.action=na.exclude))
aic.percent$draintrue[aic.percent$response=="Bacterial density"]<-Dsquared(glm(resid.ba~log(mu.scalar),family=gaussian, noargco123data), adjust=TRUE) #0.0346
aic.percent$drainfalse[aic.percent$response=="Bacterial density"]<-Dsquared(glm(resid.ba~log(mu.scalar),family=gaussian, noargco123data), adjust=FALSE)
aic.percent$raintype[aic.percent$response=="Bacterial density"]<-"general"
#total bio - rain ------------------
aic.lmxnb(round(fulldata$totalbio*10), fulldata)#m0 m1...NO RAINFALL EFFECT
## Model selection table
## (Int) log(mxv) sit log(mu.scl) log(k.scl) log(mu.scl)^2
## m0 -0.0478500 0.8689 +
## m1 0.0049660 0.8578 + -0.03733
## m2 0.0007142 0.8602 + -0.03256
## m3 0.0545100 0.8557 + -0.09147 -0.05016
## m4 0.0232100 0.8653 + -0.03210
## m9 0.0688700 0.8464 + -0.03777 -0.02133
## m10 0.0355600 0.8571 + -0.10810 -0.02098
## m6 -0.3367000 0.9174 + -0.24850
## m11 0.1271000 0.8426 + -0.09131 -0.05643 -0.04969
## m5 -0.0404700 0.8634 + -0.08543
## m12 0.0615400 0.8578 + -0.14930 -0.05495 -0.03886
## m8 0.5377000 0.8103 + -0.24220
## m7 0.1386000 0.8578 + -0.21370 -0.16440
## m13 -0.3309000 0.9125 + -0.07899 -0.23650
## m14 0.0889400 0.8772 + -0.18500 -0.23660
## m15 -0.0614600 0.8929 + -0.19380 -0.41860 -0.17230
## m16 0.2925000 0.8957 + -0.44390 -0.38920 -0.38990
## log(k.scl)^2 log(mu.scl):sit log(k.scl):sit I(log(mu.scl)^2):sit
## m0
## m1
## m2
## m3
## m4 -0.18550
## m9
## m10 -0.11480
## m6 +
## m11
## m5 +
## m12 -0.08395
## m8 -0.85460 +
## m7 + +
## m13 + +
## m14 -0.74170 + +
## m15 + + +
## m16 -1.39300 + + +
## I(log(k.scl)^2):sit log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m1
## m2
## m3
## m4
## m9 0.05376
## m10 0.05208 0.2306
## m6
## m11 0.09548
## m5
## m12 0.09499 0.1881
## m8 +
## m7
## m13 0.04280
## m14 + 0.03473 0.3150
## m15 0.11140
## m16 + 0.17570 0.9443
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2
## m0
## m1
## m2
## m3
## m4
## m9
## m10
## m6
## m11 0.04058
## m5
## m12 0.04026 -0.03766
## m8
## m7
## m13
## m14
## m15 0.17000
## m16 0.15660 0.82520
## log(k.scl):log(mu.scl):sit I(log(k.scl)^2):log(mu.scl):sit
## m0
## m1
## m2
## m3
## m4
## m9
## m10
## m6
## m11
## m5
## m12
## m8
## m7
## m13 +
## m14 + +
## m15 +
## m16 + +
## I(log(mu.scl)^2):log(k.scl):sit I(log(k.scl)^2):I(log(mu.scl)^2):sit
## m0
## m1
## m2
## m3
## m4
## m9
## m10
## m6
## m11
## m5
## m12
## m8
## m7
## m13
## m14
## m15 +
## m16 + +
## family init.theta df logLik AICc delta weight
## m0 NB(2.5231) 2.52 9 -1254.518 2527.9 0.00 0.397
## m1 NB(2.5318) 2.53 10 -1254.138 2529.4 1.44 0.193
## m2 NB(2.525) 2.52 10 -1254.435 2530.0 2.04 0.143
## m3 NB(2.5488) 2.55 11 -1253.379 2530.1 2.15 0.135
## m4 NB(2.5355) 2.54 11 -1253.978 2531.3 3.35 0.074
## m9 NB(2.539) 2.54 12 -1253.817 2533.2 5.28 0.028
## m10 NB(2.5655) 2.57 14 -1252.633 2535.4 7.48 0.009
## m6 NB(2.617) 2.62 16 -1250.365 2535.5 7.61 0.009
## m11 NB(2.5594) 2.56 14 -1252.910 2536.0 8.04 0.007
## m5 NB(2.5931) 2.59 16 -1251.437 2537.7 9.76 0.003
## m12 NB(2.5878) 2.59 17 -1251.654 2540.5 12.56 0.001
## m8 NB(2.7566) 2.76 23 -1244.494 2540.9 12.99 0.001
## m7 NB(2.6637) 2.66 23 -1248.438 2548.8 20.88 0.000
## m13 NB(2.7363) 2.74 30 -1245.394 2561.2 33.24 0.000
## m14 NB(2.9599) 2.96 44 -1236.385 2584.8 56.83 0.000
## m15 NB(2.8421) 2.84 44 -1241.162 2594.3 66.39 0.000
## m16 NB(3.2386) 3.24 65 -1226.286 2642.2 114.22 0.000
## Abbreviations:
## family: NB(2.5231) = 'Negative Binomial(2.5231)',
## NB(2.525) = 'Negative Binomial(2.525)',
## NB(2.5318) = 'Negative Binomial(2.5318)',
## NB(2.5355) = 'Negative Binomial(2.5355)',
## NB(2.539) = 'Negative Binomial(2.539)',
## NB(2.5488) = 'Negative Binomial(2.5488)',
## NB(2.5594) = 'Negative Binomial(2.5594)',
## NB(2.5655) = 'Negative Binomial(2.5655)',
## NB(2.5878) = 'Negative Binomial(2.5878)',
## NB(2.5931) = 'Negative Binomial(2.5931)',
## NB(2.617) = 'Negative Binomial(2.617)',
## NB(2.6637) = 'Negative Binomial(2.6637)',
## NB(2.7363) = 'Negative Binomial(2.7363)',
## NB(2.7566) = 'Negative Binomial(2.7566)',
## NB(2.8421) = 'Negative Binomial(2.8421)',
## NB(2.9599) = 'Negative Binomial(2.9599)',
## NB(3.2386) = 'Negative Binomial(3.2386)'
## Models ranked by AICc(x)
aic.lmx(round(fulldata$totalbio*10), family=negative.binomial(theta = 3.2386), fulldata) #mo m1 m3 m2
## Model selection table
## (Int) log(mxv) sit log(mu.scl) log(k.scl) log(mu.scl)^2
## m0 -0.03629 0.8669 +
## m1 0.01680 0.8558 + -0.03744
## m3 0.06625 0.8537 + -0.09158 -0.05015
## m2 0.01225 0.8582 + -0.03266
## m4 0.03446 0.8634 + -0.03222
## m9 0.08062 0.8443 + -0.03787 -0.02139
## m6 -0.32570 0.9155 + -0.24780
## m10 0.04779 0.8551 + -0.10790 -0.02105
## m11 0.13850 0.8406 + -0.09141 -0.05641 -0.04968
## m5 -0.03067 0.8617 + -0.08566
## m8 0.54630 0.8089 + -0.24190
## m12 0.07319 0.8558 + -0.14900 -0.05494 -0.03871
## m7 0.14880 0.8561 + -0.21450 -0.16470
## m13 -0.32160 0.9109 + -0.07923 -0.23600
## m14 0.09884 0.8755 + -0.18510 -0.23630
## m15 -0.05272 0.8914 + -0.19440 -0.41770 -0.17240
## m16 0.29220 0.8958 + -0.44390 -0.38920 -0.38990
## log(k.scl)^2 log(mu.scl):sit log(k.scl):sit I(log(mu.scl)^2):sit
## m0
## m1
## m3
## m2
## m4 -0.18610
## m9
## m6 +
## m10 -0.11570
## m11
## m5 +
## m8 -0.85530 +
## m12 -0.08448
## m7 + +
## m13 + +
## m14 -0.74220 + +
## m15 + + +
## m16 -1.39300 + + +
## I(log(k.scl)^2):sit log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m1
## m3
## m2
## m4
## m9 0.05376
## m6
## m10 0.05212 0.2296
## m11 0.09544
## m5
## m8 +
## m12 0.09497 0.1868
## m7
## m13 0.04265
## m14 + 0.03460 0.3147
## m15 0.11140
## m16 + 0.17570 0.9443
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2
## m0
## m1
## m3
## m2
## m4
## m9
## m6
## m10
## m11 0.04051
## m5
## m8
## m12 0.04019 -0.03806
## m7
## m13
## m14
## m15 0.16980
## m16 0.15660 0.82520
## log(k.scl):log(mu.scl):sit I(log(k.scl)^2):log(mu.scl):sit
## m0
## m1
## m3
## m2
## m4
## m9
## m6
## m10
## m11
## m5
## m8
## m12
## m7
## m13 +
## m14 + +
## m15 +
## m16 + +
## I(log(mu.scl)^2):log(k.scl):sit I(log(k.scl)^2):I(log(mu.scl)^2):sit
## m0
## m1
## m3
## m2
## m4
## m9
## m6
## m10
## m11
## m5
## m8
## m12
## m7
## m13
## m14
## m15 +
## m16 + +
## df logLik AICc delta weight
## m0 8 -1258.230 2533.2 0.00 0.346
## m1 9 -1257.741 2534.4 1.21 0.189
## m3 10 -1256.778 2534.7 1.49 0.165
## m2 9 -1258.123 2535.1 1.97 0.129
## m4 10 -1257.536 2536.2 3.00 0.077
## m9 11 -1257.332 2538.0 4.82 0.031
## m6 15 -1253.025 2538.5 5.35 0.024
## m10 13 -1255.840 2539.5 6.36 0.014
## m11 13 -1256.186 2540.2 7.05 0.010
## m5 15 -1254.341 2541.2 7.98 0.006
## m8 22 -1245.980 2541.4 8.19 0.006
## m12 16 -1254.615 2544.0 10.87 0.002
## m7 22 -1250.654 2550.7 17.54 0.000
## m13 29 -1247.022 2561.7 28.54 0.000
## m14 43 -1236.835 2582.5 49.29 0.000
## m15 43 -1242.122 2593.0 59.86 0.000
## m16 64 -1226.286 2638.0 104.77 0.000
## Models ranked by AICc(x)
besttb<-glm.nb(round(totalbio*10)~log(maxvol)+site, data = fulldata)
fulldata$resid.tb<-resid(glm.nb(round(totalbio*10)~log(maxvol)+site, data=fulldata, na.action=na.exclude))
aic.percent$draintrue[aic.percent$response=="Total Invertebrates"]<-Dsquared(glm(resid.tb~log(mu.scalar),family=gaussian, fulldata), adjust=TRUE) #0
aic.percent$drainfalse[aic.percent$response=="Total Invertebrates"]<-Dsquared(glm(resid.tb~log(mu.scalar),family=gaussian, fulldata), adjust=FALSE) #0
aic.percent$raintype[aic.percent$response=="Total Invertebrates"]<-"ns"
#plots ---------------
visreg(bestnit, "k.scalar", by="site", ylab="Nitrogen uptake", overlay=TRUE, partial=FALSE, band=FALSE)
visreg(bestsh, "k.scalar", by="site", ylab="Shredder biomass", overlay=TRUE, partial=FALSE, band=FALSE)
visreg(bestff, "mu.scalar", by="site", ylab="Filter feeder biomass",overlay=TRUE, partial=FALSE, band=FALSE)
visreg(betterff, "mu.scalar", by="site", ylab="Filter feeder biomass",overlay=TRUE, partial=FALSE, band=FALSE)
visreg(bestsc, "k.scalar", by="site", ylab="Scraper biomass",overlay=TRUE, partial=FALSE, band=FALSE)
visreg(bestga, "k.scalar", by="site", ylab="Gatherer biomass",overlay=TRUE, partial=FALSE, band=FALSE)
visreg(betterga, "k.scalar", by="site", ylab="Gatherer biomass", overlay=TRUE, partial=FALSE, band=FALSE)
visreg(bestpi, "k.scalar", by="site", ylab="Piercer biomass",overlay=TRUE, partial=FALSE, band=FALSE)
visreg(bestba, "mu.scalar", by="site", ylab="Bacterial biomass",overlay=TRUE, partial=FALSE, band=FALSE)
#===best Hydrology models-------------------------
#decomp- hydro
fulldatatemp<-filter(fulldata, mean_temp%nin%NA)%>%filter(cv_mean_temp%nin%NA)%>%
filter(cv.depth%nin%NA)%>%filter(long_dry%nin%NA)%>%filter(last_wet%nin%NA)%>%
filter(prop.overflow.days%nin%NA)%>%filter(prop.driedout.days%nin%NA)
aic.hydro(sqrt(fulldatatemp$decomp), sqrt(fulldatatemp$decomp)~log(fulldatatemp$maxvol)+fulldatatemp$site, gaussian, fulldatatemp)
## Warning in model.sel.default(m0, m1, m2, m3, m4, m5, m6, m7, m8, m17,
## m18, : response differs between models
## Model selection table
## (Int) fll$sit log(fll$mxv) cv.dpt log(mxv) sit prp.ovr.dys
## m3 0.7171 0.005995 +
## m5 0.6955 0.008681 +
## m22 0.6748 0.011380 +
## m23 0.6310 0.017080 +
## m6 0.6713 0.012650 +
## m17 0.6438 -6.946e-06 0.014990 +
## m19 0.6792 0.010780 +
## m4 0.6874 0.004481 +
## m1 0.6905 -1.344e-04 0.009172 +
## m0 0.6553 + 0.01288
## m7 0.6635 0.011450 +
## m21 0.6696 0.012140 +
## m2 0.6564 0.012300 + 0.01996
## m8 0.6552 0.012890 +
## m24 0.6822 0.008208 +
## m20 0.6180 0.015630 +
## m18 0.6665 0.011390 + -0.02346
## prp.drd.dys men.dpt lng_dry lst_wet chn_cv_tmp chn_men_tmp
## m3 -0.07976
## m5 -0.002556
## m22 -0.0004333
## m23 0.009758
## m6 -0.0005875
## m17
## m19 -0.04258
## m4 0.0004895
## m1
## m0
## m7 0.004223
## m21 -0.001606
## m2
## m8 -0.0000274
## m24 0.0041790
## m20 0.0006445
## m18
## cv.dpt:sit prp.ovr.dys:sit prp.drd.dys:sit men.dpt:sit lng_dry:sit
## m3
## m5
## m22
## m23
## m6
## m17 +
## m19 +
## m4
## m1
## m0
## m7
## m21 +
## m2
## m8
## m24
## m20 +
## m18 +
## lst_wet:sit chn_cv_tmp:sit chn_men_tmp:sit df logLik AICc delta
## m3 9 245.867 -472.6 0.00
## m5 9 245.691 -472.3 0.35
## m22 + 14 250.570 -470.5 2.13
## m23 + 14 249.558 -468.5 4.15
## m6 9 243.273 -467.5 5.19
## m17 14 249.002 -467.4 5.26
## m19 14 248.604 -466.6 6.06
## m4 9 242.701 -466.3 6.33
## m1 9 242.613 -466.1 6.51
## m0 8 241.380 -465.9 6.75
## m7 9 241.977 -464.9 7.78
## m21 14 247.538 -464.5 8.19
## m2 9 241.473 -463.9 8.79
## m8 9 241.380 -463.7 8.97
## m24 + 14 245.966 -461.3 11.34
## m20 14 244.971 -459.3 13.33
## m18 14 243.690 -456.8 15.89
## weight
## m3 0.373
## m5 0.312
## m22 0.129
## m23 0.047
## m6 0.028
## m17 0.027
## m19 0.018
## m4 0.016
## m1 0.014
## m0 0.013
## m7 0.008
## m21 0.006
## m2 0.005
## m8 0.004
## m24 0.001
## m20 0.000
## m18 0.000
## Models ranked by AICc(x)
#m3 m22
bestmoddecomp<-glm(sqrt(decomp)~log(maxvol)+site+prop.driedout.days, family=gaussian, data = nocadata)#m3
nextmoddecomp<-glm(sqrt(decomp)~log(maxvol)+site*last_wet, family=gaussian, data = fulldata)#divergent effects ma, pr, cr
visreg(bestmoddecomp, "prop.driedout.days", by="site", ylab="Decomposition", overlay=TRUE, partial=FALSE, band=FALSE)
nocadata$resid.decomp.hydro<-resid(glm(sqrt(decomp)~log(maxvol)+site,data=nocadata, na.action=na.exclude))
aic.percent$dhydrotrue[aic.percent$response=="Decomposition"]<-Dsquared(glm(resid.decomp.hydro~prop.driedout.days,family=gaussian, nocadata), adjust=TRUE) #0.02556
aic.percent$dhydrofalse[aic.percent$response=="Decomposition"]<-Dsquared(glm(resid.decomp.hydro~prop.driedout.days,family=gaussian, nocadata), adjust=FALSE)
aic.percent$hydrotype[aic.percent$response=="Decomposition"]<-"general"
aic.percent$bestmodel[aic.percent$response=="Decomposition"]<-"hydrology"
#nitrogen - hydro---------------------------
no126datatemp<-filter(no126data, mean_temp%nin%NA)%>%filter(cv_mean_temp%nin%NA)%>%
filter(cv.depth%nin%NA)%>%filter(long_dry%nin%NA)%>%filter(last_wet%nin%NA)%>%
filter(prop.overflow.days%nin%NA)%>%filter(prop.driedout.days%nin%NA)
aic.hydro((no126datatemp$n15.bromeliad.final+4)^0.125, (no126datatemp$n15.bromeliad.final+4)^0.125~log(no126datatemp$maxvol)+no126datatemp$site*(log(no126datatemp$mu.scalar)+I(log(no126datatemp$mu.scalar)^2)), gaussian, no126datatemp)
## Warning in model.sel.default(m0, m1, m2, m3, m4, m5, m6, m7, m8, m17,
## m18, : response differs between models
## Model selection table
## (Int) log(n12$mxv) log(n12$mu.scl) log(n12$mu.scl)^2 n12$sit
## m21 1.626
## m20 1.500
## m19 1.675
## m1 1.661
## m3 1.652
## m6 1.605
## m5 1.632
## m2 1.620
## m4 1.626
## m8 1.618
## m7 1.618
## m18 1.564
## m17 1.610
## m22 1.647
## m0 1.600 -0.01249 0.03482 -0.004088 +
## m24 1.571
## m23 1.596
## log(n12$mu.scl):n12$sit I(log(n12$mu.scl)^2):n12$sit cv.dpt
## m21
## m20
## m19
## m1 -0.0001658
## m3
## m6
## m5
## m2
## m4
## m8
## m7
## m18
## m17 -0.0002792
## m22
## m0 + +
## m24
## m23
## log(mxv) sit prp.ovr.dys prp.drd.dys men.dpt lng_dry lst_wet
## m21 -0.010740 + -0.0079200
## m20 -0.013040 + 0.0027130
## m19 -0.017540 + -0.21320
## m1 -0.022350 +
## m3 -0.021670 + -0.04501
## m6 -0.017600 + 0.0004596
## m5 -0.019340 + -0.0009513
## m2 -0.019180 + 0.04847
## m4 -0.020080 + 0.0001343
## m8 -0.017880 +
## m7 -0.017830 +
## m18 -0.017370 + 0.45410
## m17 -0.011490 +
## m22 -0.016220 + -0.0015500
## m0
## m24 -0.009705 +
## m23 -0.014040 +
## chn_cv_tmp chn_men_tmp cv.dpt:sit prp.ovr.dys:sit prp.drd.dys:sit
## m21
## m20
## m19 +
## m1
## m3
## m6
## m5
## m2
## m4
## m8 0.0002202
## m7 0.0001738
## m18 +
## m17 +
## m22
## m0
## m24 0.0007984
## m23 0.0013310
## men.dpt:sit lng_dry:sit lst_wet:sit chn_cv_tmp:sit chn_men_tmp:sit df
## m21 + 14
## m20 + 14
## m19 14
## m1 9
## m3 9
## m6 9
## m5 9
## m2 9
## m4 9
## m8 9
## m7 9
## m18 14
## m17 14
## m22 + 14
## m0 20
## m24 + 14
## m23 + 14
## logLik AICc delta weight
## m21 165.229 -299.8 0.00 0.234
## m20 164.704 -298.8 1.05 0.139
## m19 164.551 -298.5 1.36 0.119
## m1 158.512 -297.9 1.89 0.091
## m3 158.328 -297.6 2.26 0.076
## m6 158.231 -297.4 2.45 0.069
## m5 158.011 -296.9 2.89 0.055
## m2 157.999 -296.9 2.92 0.054
## m4 157.823 -296.5 3.27 0.046
## m8 157.798 -296.5 3.32 0.045
## m7 157.785 -296.5 3.34 0.044
## m18 162.174 -293.7 6.11 0.011
## m17 161.848 -293.1 6.76 0.008
## m22 161.522 -292.4 7.41 0.006
## m0 168.104 -290.7 9.10 0.002
## m24 159.816 -289.0 10.83 0.001
## m23 159.495 -288.3 11.47 0.001
## Models ranked by AICc(x)
#bestmodel is long_dry*site (m21) followed by m8 (site+change_mean_temp)
bestmodN2<-glm((n15.bromeliad.final+4)^0.125~log(maxvol)+site*long_dry, family=gaussian, data = no126datatemp)
visreg(bestmodN2, "long_dry", by="site")#driven mainly by neg effect in argentina vs tiny positive effect macae, pr; other sites have hardly any variance in longest dry
visreg(bestmodN2, "long_dry", by="site",ylab="Nitrogen uptake", overlay=FALSE, partial=TRUE, band=FALSE)
Anova(bestmodN2, type=2)#most sites increase N uptake with long dry, only FG and one other goes down
## Analysis of Deviance Table (Type II tests)
##
## Response: (n15.bromeliad.final + 4)^0.125
## LR Chisq Df Pr(>Chisq)
## log(maxvol) 0.206 1 0.64962
## site 254.502 5 < 2e-16 ***
## long_dry 0.456 1 0.49947
## site:long_dry 13.927 5 0.01608 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
nextbestmodN<-glm((n15.bromeliad.final+4)^0.125~log(maxvol)+site+(change_mean_temp), family=gaussian, data = no126datatemp)
visreg(nextbestmodN, "change_mean_temp", by="site",ylab="Nitrogen uptake", overlay=TRUE, partial=FALSE, band=FALSE)
Anova(nextbestmodN, type=2)# but change mean temp is p=0.06, so not great
## Analysis of Deviance Table (Type II tests)
##
## Response: (n15.bromeliad.final + 4)^0.125
## LR Chisq Df Pr(>Chisq)
## log(maxvol) 0.591 1 0.4422
## site 240.844 5 <2e-16 ***
## change_mean_temp 0.026 1 0.8720
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
no126data$resid.nit.hydro<-resid(glm((n15.bromeliad.final+4)^0.125~log(maxvol)+site,data=no126data, na.action=na.exclude))
aic.percent$dhydrotrue[aic.percent$response=="Nitrogen uptake"]<-Dsquared(glm(resid.nit.hydro~site*long_dry,family=gaussian, no126data), adjust=TRUE) #0.0269
aic.percent$dhydrofalse[aic.percent$response=="Nitrogen uptake"]<-Dsquared(glm(resid.nit.hydro~site*long_dry,family=gaussian, no126data), adjust=FALSE)
aic.percent$hydrotype[aic.percent$response=="Nitrogen uptake"]<-"contingent"
aic.percent$bestmodel[aic.percent$response=="Nitrogen uptake"]<-"hydrology"
#co2 - hydro-----------------------
nocaprdatatemp<-filter(nocaprdata, mean_temp%nin%NA)%>%filter(cv_mean_temp%nin%NA)%>%
filter(cv.depth%nin%NA)%>%filter(long_dry%nin%NA)%>%filter(last_wet%nin%NA)%>%
filter(prop.overflow.days%nin%NA)%>%filter(prop.driedout.days%nin%NA)
aic.hydro(log(nocaprdatatemp$co2.final), log(nocaprdatatemp$co2.final)~log(nocaprdatatemp$maxvol)+nocaprdatatemp$site, gaussian, nocaprdatatemp)
## Warning in model.sel.default(m0, m1, m2, m3, m4, m5, m6, m7, m8, m17,
## m18, : response differs between models
## Model selection table
## (Int) log(ncp$mxv) ncp$sit cv.dpt log(mxv) sit prp.ovr.dys
## m0 0.24900 0.03322 +
## m6 0.28000 0.03608 +
## m7 0.18170 0.04488 +
## m4 0.31800 0.01453 +
## m3 0.32320 0.02507 +
## m2 0.23630 0.03852 + -0.1591
## m8 0.23840 0.03505 +
## m1 0.30000 -1.819e-04 0.02762 +
## m5 0.23660 0.03416 +
## m23 0.29780 0.02474 +
## m21 0.05183 0.05736 +
## m24 0.23610 0.03545 +
## m22 0.26840 0.02494 +
## m20 0.35050 0.03322 +
## m19 0.28920 0.01932 +
## m17 0.20950 -6.230e-06 0.04016 +
## m18 0.23900 0.04216 + -0.3691
## prp.drd.dys men.dpt lng_dry lst_wet chn_cv_tmp chn_men_tmp
## m0
## m6 -0.001901
## m7 -0.020820
## m4 0.001162
## m3 -0.0983
## m2
## m8 -0.002492
## m1
## m5 0.001108
## m23 -0.003022
## m21 0.009259
## m24 -0.001394
## m22 0.001129
## m20 -0.003050
## m19 0.1438
## m17
## m18
## cv.dpt:sit prp.ovr.dys:sit prp.drd.dys:sit men.dpt:sit lng_dry:sit
## m0
## m6
## m7
## m4
## m3
## m2
## m8
## m1
## m5
## m23
## m21 +
## m24
## m22
## m20 +
## m19 +
## m17 +
## m18 +
## lst_wet:sit chn_cv_tmp:sit chn_men_tmp:sit df logLik AICc delta
## m0 7 -67.062 149.0 0.00
## m6 8 -66.728 150.6 1.60
## m7 8 -66.742 150.7 1.63
## m4 8 -66.940 151.1 2.03
## m3 8 -66.946 151.1 2.04
## m2 8 -66.951 151.1 2.05
## m8 8 -66.960 151.1 2.07
## m1 8 -67.016 151.2 2.18
## m5 8 -67.054 151.3 2.25
## m23 + 12 -63.318 153.3 4.25
## m21 11 -65.017 154.3 5.22
## m24 + 12 -64.264 155.2 6.14
## m22 + 12 -65.754 158.2 9.12
## m20 12 -65.938 158.5 9.49
## m19 12 -66.011 158.7 9.63
## m17 12 -66.412 159.5 10.43
## m18 12 -66.733 160.1 11.08
## weight
## m0 0.234
## m6 0.105
## m7 0.104
## m4 0.085
## m3 0.085
## m2 0.084
## m8 0.083
## m1 0.079
## m5 0.076
## m23 0.028
## m21 0.017
## m24 0.011
## m22 0.002
## m20 0.002
## m19 0.002
## m17 0.001
## m18 0.001
## Models ranked by AICc(x)
#m7 m0
bestmodco2<-glm(log(co2.final)~log(maxvol)+site+change_cv_temp, family=gaussian, data = nocaprdata)
visreg(bestmodco2, "change_cv_temp", by="site",ylab="CO2 flux", overlay=TRUE, partial=FALSE, band=FALSE)
Anova(bestmodco2, type=2)#marginal effect of cv temp
## Analysis of Deviance Table (Type II tests)
##
## Response: log(co2.final)
## LR Chisq Df Pr(>Chisq)
## log(maxvol) 0.14 1 0.7119
## site 1065.91 4 <2e-16 ***
## change_cv_temp 0.61 1 0.4357
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#co2 flux goes down as temperatures become more variable, but not that different from null model!
nocaprdatatemp$resid.co2.hydro<-resid(glm(log(co2.final)~log(maxvol)+site,data=nocaprdatatemp, na.action=na.exclude))
aic.percent$dhydrotrue[aic.percent$response=="CO2 flux"]<-Dsquared(glm(resid.co2.hydro~change_cv_temp,family=gaussian, nocaprdatatemp), adjust=TRUE) #0.0127
aic.percent$dhydrofalse[aic.percent$response=="CO2 flux"]<-Dsquared(glm(resid.co2.hydro~change_cv_temp,family=gaussian, nocaprdatatemp), adjust=FALSE) #0.0127
aic.percent$hydrotype[aic.percent$response=="CO2 flux"]<-"general"
aic.percent$bestmodel[aic.percent$response=="CO2 flux"]<-"hydrology"
#filter feeder - hydro ---------------------------
nococr140datatemp<-filter(nococr140data, mean_temp%nin%NA)%>%filter(cv_mean_temp%nin%NA)%>%
filter(cv.depth%nin%NA)%>%filter(long_dry%nin%NA)%>%filter(last_wet%nin%NA)%>%
filter(prop.overflow.days%nin%NA)%>%filter(prop.driedout.days%nin%NA)
nococrleaky140data<-filter(nococr140data, site_brom.id%nin%c("macae_B24", "macae_B22", "macae_B9", "macae_B2", "macae_B11", "macae_B41", "argentina_15"))
nococrleaky140data<-filter(nococr140data, site_brom.id%nin%c("costarica_26", "macae_B22", "macae_B9", "macae_B2", "macae_B41", "argentina_15"))
#new leaky still selects m1 as best
aic.hydro.nb(round(nococr140datatemp$filter.feeder_bio*100),round(nococr140datatemp$filter.feeder_bio*100)~log(nococr140datatemp$maxvol)+nococr140datatemp$site*(log(nococr140datatemp$mu.scalar)+I(log(nococr140datatemp$mu.scalar)^2)),nococr140datatemp)
## Warning in glm.nb(y ~ log(maxvol) + site + cv.depth, data = dataset):
## alternation limit reached
## Warning in glm.nb(y ~ log(maxvol) + site + cv.depth, data = dataset):
## response differs between models
## Model selection table
## (Int) log(n14$mxv) log(n14$mu.scl) log(n14$mu.scl)^2 n14$sit
## m17 1.5010
## m19 0.1171
## m21 1.3040
## m1 -1.2280
## m3 -1.7780
## m20 -1.7930
## m0 -5.5820 1.546 0.8111 -1.162 +
## m5 -2.1620
## m4 -2.0570
## m6 -3.0830
## m7 -3.1690
## m22 -3.5640
## m2 -2.9460
## m8 -3.0270
## m23 -2.7940
## m24 -3.1750
## m18 -3.5420
## log(n14$mu.scl):n14$sit I(log(n14$mu.scl)^2):n14$sit cv.dpt log(mxv)
## m17 -0.04172 0.7010
## m19 0.7122
## m21 0.4904
## m1 -0.01382 0.8411
## m3 0.8759
## m20 0.3894
## m0 + +
## m5 0.8997
## m4 0.6724
## m6 1.0790
## m7 1.0010
## m22 1.2750
## m2 0.9490
## m8 1.0000
## m23 0.9198
## m24 0.9960
## m18 0.9848
## sit prp.ovr.dys prp.drd.dys men.dpt lng_dry lst_wet chn_cv_tmp
## m17 +
## m19 + -7.931
## m21 + -0.2991
## m1 +
## m3 + -3.264
## m20 + 0.06029
## m0
## m5 + -0.0693
## m4 + 0.02272
## m6 + -0.02008
## m7 + 0.2883
## m22 + -0.04907
## m2 + 1.761
## m8 +
## m23 + 0.4897
## m24 +
## m18 + 4.995
## chn_men_tmp cv.dpt:sit prp.ovr.dys:sit prp.drd.dys:sit men.dpt:sit
## m17 +
## m19 +
## m21
## m1
## m3
## m20 +
## m0
## m5
## m4
## m6
## m7
## m22
## m2
## m8 0.01324
## m23
## m24 0.19030
## m18 +
## lng_dry:sit lst_wet:sit chn_cv_tmp:sit chn_men_tmp:sit family
## m17 NB(0.8561)
## m19 NB(0.8405)
## m21 + NB(0.7783)
## m1 NB(0.7208)
## m3 NB(0.7159)
## m20 NB(0.7263)
## m0 NB(0.7541)
## m5 NB(0.672)
## m4 NB(0.6706)
## m6 NB(0.6425)
## m7 NB(0.6316)
## m22 + NB(0.6704)
## m2 NB(0.6238)
## m8 NB(0.6158)
## m23 + NB(0.6446)
## m24 + NB(0.6442)
## m18 NB(0.63)
## init.theta df logLik AICc delta weight
## m17 0.856 10 -542.379 1106.8 0.00 0.808
## m19 0.841 10 -543.828 1109.7 2.90 0.190
## m21 0.778 10 -548.648 1119.4 12.54 0.002
## m1 0.721 7 -553.998 1123.0 16.20 0.000
## m3 0.716 7 -554.668 1124.4 17.54 0.000
## m20 0.726 10 -554.125 1130.3 23.49 0.000
## m0 0.754 14 -549.520 1131.1 24.30 0.000
## m5 0.672 7 -558.429 1131.9 25.06 0.000
## m4 0.671 7 -558.678 1132.4 25.56 0.000
## m6 0.643 7 -560.971 1137.0 30.15 0.000
## m7 0.632 7 -561.450 1137.9 31.10 0.000
## m22 0.67 10 -558.589 1139.2 32.42 0.000
## m2 0.624 7 -562.610 1140.2 33.43 0.000
## m8 0.616 7 -563.304 1141.6 34.81 0.000
## m23 0.645 10 -559.887 1141.8 35.02 0.000
## m24 0.644 10 -560.010 1142.1 35.26 0.000
## m18 0.63 10 -562.065 1146.2 39.37 0.000
## Abbreviations:
## family: NB(0.6158) = 'Negative Binomial(0.6158)',
## NB(0.6238) = 'Negative Binomial(0.6238)',
## NB(0.63) = 'Negative Binomial(0.63)',
## NB(0.6316) = 'Negative Binomial(0.6316)',
## NB(0.6425) = 'Negative Binomial(0.6425)',
## NB(0.6442) = 'Negative Binomial(0.6442)',
## NB(0.6446) = 'Negative Binomial(0.6446)',
## NB(0.6704) = 'Negative Binomial(0.6704)',
## NB(0.6706) = 'Negative Binomial(0.6706)',
## NB(0.672) = 'Negative Binomial(0.672)',
## NB(0.7159) = 'Negative Binomial(0.7159)',
## NB(0.7208) = 'Negative Binomial(0.7208)',
## NB(0.7263) = 'Negative Binomial(0.7263)',
## NB(0.7541) = 'Negative Binomial(0.7541)',
## NB(0.7783) = 'Negative Binomial(0.7783)',
## NB(0.8405) = 'Negative Binomial(0.8405)',
## NB(0.8561) = 'Negative Binomial(0.8561)'
## Models ranked by AICc(x)
#best model is m17, m26, m19: cv.depth*site, exposure*site, proportion driedoput days*site,
#note that if we remove th 6 leaky bromeliads in macae (and argentina cv.depthoutlier), then site+cv.depth is the best model
# aic.hydro.nb(round(nococrleaky140datatemp$filter.feeder_bio*100),round(nococrleaky140datatemp$filter.feeder_bio*100)~log(nococrleaky140datatemp$maxvol)+nococrleaky140datatemp$site*(log(nococrleaky140datatemp$mu.scalar)+I(log(nococrleaky140datatemp$mu.scalar)^2)),nococrleaky140datatemp)
bestmodff<-glm.nb(round(filter.feeder_bio*100)~log(maxvol)+site*cv.depth, data = nococr140datatemp)
par(mfrow=c(1,1)); visreg(bestmodff, "cv.depth", by="site", ylab="Filter feeder biomass", overlay=TRUE, partial=FALSE, band=FALSE)
Anova(bestmodff, type=2) #aic=957.1595
## Analysis of Deviance Table (Type II tests)
##
## Response: round(filter.feeder_bio * 100)
## LR Chisq Df Pr(>Chisq)
## log(maxvol) 1.9658 1 0.16089
## site 10.4594 3 0.01504 *
## cv.depth 23.7502 1 1.097e-06 ***
## site:cv.depth 24.7576 3 1.735e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
tapply(fulldata$cv.depth, fulldata$site, max)
## argentina cardoso colombia costarica frenchguiana
## 670.82039 NA 91.45157 118.30951 66.55870
## macae puertorico
## 178.65466 180.31614
nextbestmodff<-glm.nb(round(filter.feeder_bio*100)~log(maxvol)+site*exposure, data = nococr140datatemp)#aic=-959.661
visreg(nextbestmodff, "exposure", by="site", ylab="Filter feeder biomass", overlay=TRUE, partial=FALSE, band=FALSE)
nococr140data$resid.ff.hydro<-resid(glm.nb(round(nococr140data$filter.feeder_bio*100)~log(maxvol)+site,data=nococr140data, na.action=na.exclude))
aic.percent$dhydrotrue[aic.percent$response=="Filter feeder"]<-Dsquared(glm(resid.ff.hydro~site*cv.depth,family=gaussian, nococr140data), adjust=TRUE) #0.1954
aic.percent$dhydrofalse[aic.percent$response=="Filter feeder"]<-Dsquared(glm(resid.ff.hydro~site*cv.depth,family=gaussian, nococr140data), adjust=FALSE) #0.1954
aic.percent$hydrotype[aic.percent$response=="Filter feeder"]<-"contingent"
aic.percent$bestmodel[aic.percent$response=="Filter feeder"]<-"hydrology"
plot(sqrt(fulldata$filter.feeder_bio)~log(fulldata$cv.depth))
bestmodff<-glm.nb(round(filter.feeder_bio*100)~log(maxvol)+site*(log(cv.depth)+prop.driedout.days), data = nococr140data)
par(mfrow=c(1,1)); visreg(bestmodff, "prop.driedout.days", by="site", ylab="Filter feeder biomass", overlay=TRUE, partial=TRUE, band=FALSE)
par(mfrow=c(1,1)); visreg(bestmodff, "cv.depth", by="site", ylab="Filter feeder biomass", overlay=TRUE, partial=TRUE, band=FALSE)
bestmodff<-glm.nb(round(filter.feeder_bio*100)~log(maxvol)+site*cv.depth, data = nococr140data)
bestmodff<-glm.nb(round(Culex*100)~log(maxvol)+site*cv.depth, data = nococr140data)
## Error in eval(expr, envir, enclos): object 'Culex' not found
nococr140data$per.Wyeomyia<-ifelse(nococr140data$Culicidae_bio>0,nococr140data$Wyeomyia/nococr140data$Culicidae_bio, NA)
tapply(nococrleaky140data$cv.depth, nococrleaky140data$site, max)
## argentina cardoso colombia costarica frenchguiana
## 227.6321 NA NA NA 66.5587
## macae puertorico
## 178.6547 180.3161
bestmodff<-glm.nb(round(filter.feeder_bio*100)~log(maxvol)+site*cv.depth+per.Wyeomyia, data = nococr140data)
Anova(bestmodff)
## Analysis of Deviance Table (Type II tests)
##
## Response: round(filter.feeder_bio * 100)
## LR Chisq Df Pr(>Chisq)
## log(maxvol) 0.0002 1 0.98896
## site 11.2754 3 0.01033 *
## cv.depth 3.2871 1 0.06983 .
## per.Wyeomyia 5.0680 1 0.02437 *
## site:cv.depth 6.5963 3 0.08594 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot(madata$prop.driedout.days~madata$cv.depth)
par(mfrow=c(1,1)); visreg(bestmodff, "per.Wyeomyia", by="site",ylab="Filter feeder biomass", overlay=TRUE, partial=TRUE, band=FALSE)
View(madata%>%select(cv.depth, filter.feeder_bio, site_brom.id))#the outlier macae are all where 1 out of the 3 leaves is zero, but rest fine
View(nococr140data%>%select(cv.depth, filter.feeder_bio, site_brom.id))
bestmodff<-glm.nb(round(filter.feeder_bio*100)~log(maxvol)+site*cv.depth+per.Wyeomyia, data = nococr140data)
#and now site x cv.depth has disappeared!
bestmodff<-glm.nb(round(filter.feeder_bio*100)~log(maxvol)+site+cv.depth+per.Wyeomyia, data = nococr140data)
summary(bestmodff); Dsquared(bestmodff, adjust=TRUE); Anova(bestmodff) #tested for cv.depth x per.Wyemyia int, not sig
##
## Call:
## glm.nb(formula = round(filter.feeder_bio * 100) ~ log(maxvol) +
## site + cv.depth + per.Wyeomyia, data = nococr140data, init.theta = 1.976072519,
## link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.3282 -0.9778 -0.1173 0.4185 2.3377
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.247245 2.133658 1.053 0.2922
## log(maxvol) 0.272608 0.356530 0.765 0.4445
## sitefrenchguiana -0.441258 0.347095 -1.271 0.2036
## sitemacae 1.236785 0.430918 2.870 0.0041 **
## sitepuertorico 0.233400 0.273799 0.852 0.3940
## cv.depth -0.004967 0.003102 -1.601 0.1094
## per.Wyeomyia 0.561183 0.250877 2.237 0.0253 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(1.9761) family taken to be 1)
##
## Null deviance: 213.23 on 95 degrees of freedom
## Residual deviance: 104.20 on 89 degrees of freedom
## (53 observations deleted due to missingness)
## AIC: 993.5
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 1.976
## Std. Err.: 0.283
##
## 2 x log-likelihood: -977.496
## [1] 0.4724648
## Analysis of Deviance Table (Type II tests)
##
## Response: round(filter.feeder_bio * 100)
## LR Chisq Df Pr(>Chisq)
## log(maxvol) 0.6109 1 0.43444
## site 10.6050 3 0.01407 *
## cv.depth 3.0969 1 0.07844 .
## per.Wyeomyia 3.6571 1 0.05583 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#aha, if per.Wyeomyia->NA where no mosquitoes, then lose that data from model
#but even so, per wyeomyia and cv depth both still very sig, pretty robust model
#explored extensively other version of filter feeder hydro model, site x cv.depth still best (non-convergent models removed)
aic.filter<-function(y, dataset)
{
m1<-glm.nb(y~log(maxvol)+site+cv.depth, data = dataset)
m2<-glm.nb(y~log(maxvol)+site+cv.depth+per.Wyeomyia, data=dataset)
m3<-glm.nb(y~log(maxvol)+site+cv.depth+I(cv.depth^2), data = dataset)
m4<-glm.nb(y~log(maxvol)+site+log(cv.depth), data = dataset)
m5<-glm.nb(y~log(maxvol)+site+prop.driedout.days, data = dataset)
m6<-glm.nb(y~log(maxvol)+site+prop.driedout.days+cv.depth, data = dataset)
m8<-glm.nb(y~log(maxvol)+site+sqrt(prop.driedout.days)+log(cv.depth), data = dataset)
m11<-glm.nb(y~log(maxvol)+site*cv.depth, data = dataset)
m12<-glm.nb(y~log(maxvol)+site*(cv.depth+per.Wyeomyia), data=dataset)
m14<-glm.nb(y~log(maxvol)+site*log(cv.depth), data = dataset)
m15<-glm.nb(y~log(maxvol)+site*prop.driedout.days, data = dataset)
m16<-glm.nb(y~log(maxvol)+site*(prop.driedout.days+cv.depth),data = dataset)
m18<-glm.nb(y~log(maxvol)+site*(sqrt(prop.driedout.days)+log(cv.depth)), data = dataset)
m19<-glm.nb(y~log(maxvol)+site*(log(cv.depth)+prop.driedout.days), data = dataset)
print(aicset<-model.sel(m1,m2, m3, m4, m5, m6, m8, m11, m12, m14, m15, m16, m18,m19))
}
aic.filter(round(nococr140data$filter.feeder_bio*100),nococr140data) #the general per.Wyeomyia selected
## Warning in glm.nb(y ~ log(maxvol) + site + cv.depth, data = dataset):
## alternation limit reached
## Warning: glm.fit: algorithm did not converge
## Warning in glm.nb(y ~ log(maxvol) + site + cv.depth + I(cv.depth^2), data =
## dataset): alternation limit reached
## Warning in glm.nb(y ~ log(maxvol) + site + prop.driedout.days + cv.depth, :
## alternation limit reached
## Warning in model.sel.default(m1, m2, m3, m4, m5, m6, m8, m11, m12, m14, :
## models are not all fitted to the same data
## Model selection table
## (Int) cv.dpt log(mxv) sit per.Wym cv.dpt^2 log(cv.dpt)
## m12 3.7490 0.0006211 -0.0387 + 1.5730
## m2 2.2470 -0.0049670 0.2726 + 0.5612
## m11 1.5040 -0.0416400 0.6996 +
## m19 -2.6070 0.6008 + 0.9222
## m15 0.1200 0.7112 +
## m16 2.0520 -0.0485600 0.6360 +
## m1 -1.2350 -0.0136900 0.8410 +
## m14 9.4080 0.6805 + -2.5580
## m5 -1.7810 0.8757 +
## m3 -1.6100 -0.0087660 0.8738 + -2.652e-05
## m6 -1.3440 -0.0096720 0.8429 +
## m18 9.9460 0.6164 + -2.6150
## m4 1.6670 0.7333 + -0.7782
## m8 0.7884 0.7299 + -0.4734
## prp.drd.dys sqr(prp.drd.dys) cv.dpt:sit per.Wym:sit log(cv.dpt):sit
## m12 + +
## m2
## m11 +
## m19 -10.260 +
## m15 -7.916
## m16 1.560 +
## m1
## m14 +
## m5 -3.239
## m3
## m6 -0.952
## m18 0.1811 +
## m4
## m8 -0.9259
## prp.drd.dys:sit sit:sqr(prp.drd.dys) family init.theta df logLik
## m12 NB(2.3949) 2.39 13 -479.268
## m2 NB(1.9761) 1.98 8 -488.748
## m11 NB(0.8662) 0.866 10 -548.563
## m19 + NB(0.9388) 0.939 14 -544.079
## m15 + NB(0.8505) 0.851 10 -550.019
## m16 + NB(0.9244) 0.924 14 -545.235
## m1 NB(0.7284) 0.728 7 -560.354
## m14 NB(0.7675) 0.768 10 -556.851
## m5 NB(0.7237) 0.724 7 -561.019
## m3 NB(0.7283) 0.728 8 -560.254
## m6 NB(0.7293) 0.729 8 -560.297
## m18 + NB(0.8018) 0.802 14 -554.521
## m4 NB(0.697) 0.697 7 -563.459
## m8 NB(0.7003) 0.7 8 -563.213
## AICc delta weight
## m12 989.0 0.00 0.956
## m2 995.2 6.18 0.044
## m11 1119.2 130.19 0.000
## m19 1120.2 131.22 0.000
## m15 1122.1 133.10 0.000
## m16 1122.5 133.53 0.000
## m1 1135.7 146.74 0.000
## m14 1135.7 146.76 0.000
## m5 1137.0 148.07 0.000
## m3 1137.8 148.84 0.000
## m6 1137.9 148.93 0.000
## m18 1141.1 152.10 0.000
## m4 1141.9 152.95 0.000
## m8 1143.7 154.76 0.000
## Abbreviations:
## family: NB(0.697) = 'Negative Binomial(0.697)',
## NB(0.7003) = 'Negative Binomial(0.7003)',
## NB(0.7237) = 'Negative Binomial(0.7237)',
## NB(0.7283) = 'Negative Binomial(0.7283)',
## NB(0.7284) = 'Negative Binomial(0.7284)',
## NB(0.7293) = 'Negative Binomial(0.7293)',
## NB(0.7675) = 'Negative Binomial(0.7675)',
## NB(0.8018) = 'Negative Binomial(0.8018)',
## NB(0.8505) = 'Negative Binomial(0.8505)',
## NB(0.8662) = 'Negative Binomial(0.8662)',
## NB(0.9244) = 'Negative Binomial(0.9244)',
## NB(0.9388) = 'Negative Binomial(0.9388)',
## NB(1.9761) = 'Negative Binomial(1.9761)',
## NB(2.3949) = 'Negative Binomial(2.3949)'
## Models ranked by AICc(x)
#shredder - hydro -------------------
aic.hydro.nb(round(fulldatatemp$shredder_bio*10), round(fulldatatemp$shredder_bio*10)~log(fulldatatemp$maxvol)+fulldatatemp$site+log(fulldatatemp$k.scalar)+I(log(fulldatatemp$k.scalar)^2), fulldatatemp)
## Warning in model.sel.default(m0, m1, m2, m3, m4, m5, m6, m7, m8, m17,
## m18, : response differs between models
## Model selection table
## (Int) fll$sit log(fll$k.scl) log(fll$k.scl)^2 log(fll$mxv) cv.dpt
## m0 1.205 + 0.2818 -0.6104 0.237
## m2 1.617
## m8 1.695
## m4 1.095
## m7 1.667
## m6 1.618
## m1 1.755 -0.000799
## m5 1.536
## m3 1.623
## m23 1.183
## m22 1.923
## m18 1.782
## m20 2.038
## m24 1.311
## m19 1.410
## m21 1.442
## m17 1.898 -0.000241
## log(mxv) sit prp.ovr.dys prp.drd.dys men.dpt lng_dry lst_wet
## m0
## m2 0.16080 + -1.3650
## m8 0.12190 +
## m4 0.25760 + -0.006433
## m7 0.12610 +
## m6 0.14210 + -0.002009
## m1 0.12460 +
## m5 0.14330 + 0.004066
## m3 0.13530 + -0.03993
## m23 0.20990 +
## m22 0.13890 + -0.014010
## m18 0.11950 + -0.7314
## m20 0.15320 + -0.017550
## m24 0.18730 +
## m19 0.11850 + 0.98050
## m21 0.11930 + 0.039280
## m17 0.08938 +
## chn_cv_tmp chn_men_tmp cv.dpt:sit prp.ovr.dys:sit prp.drd.dys:sit
## m0
## m2
## m8 0.01509
## m4
## m7 0.02570
## m6
## m1
## m5
## m3
## m23 -0.01157
## m22
## m18 +
## m20
## m24 -0.01034
## m19 +
## m21
## m17 +
## men.dpt:sit lng_dry:sit lst_wet:sit chn_cv_tmp:sit chn_men_tmp:sit
## m0
## m2
## m8
## m4
## m7
## m6
## m1
## m5
## m3
## m23 +
## m22 +
## m18
## m20 +
## m24 +
## m19
## m21 +
## m17
## family init.theta df logLik AICc delta weight
## m0 NB(1.2108) 1.21 10 -680.799 1382.9 0.00 0.497
## m2 NB(1.1759) 1.18 9 -683.066 1385.2 2.28 0.159
## m8 NB(1.1619) 1.16 9 -683.883 1386.9 3.92 0.070
## m4 NB(1.1633) 1.16 9 -683.901 1386.9 3.95 0.069
## m7 NB(1.1524) 1.15 9 -684.484 1388.1 5.12 0.038
## m6 NB(1.1501) 1.15 9 -684.511 1388.1 5.17 0.037
## m1 NB(1.1511) 1.15 9 -684.515 1388.1 5.18 0.037
## m5 NB(1.151) 1.15 9 -684.553 1388.2 5.26 0.036
## m3 NB(1.1505) 1.15 9 -684.587 1388.3 5.33 0.035
## m23 NB(1.2185) 1.22 14 -680.154 1390.9 7.99 0.009
## m22 NB(1.2008) 1.2 14 -680.701 1392.0 9.09 0.005
## m18 NB(1.2021) 1.2 14 -681.257 1393.1 10.20 0.003
## m20 NB(1.1946) 1.19 14 -681.582 1393.8 10.85 0.002
## m24 NB(1.1956) 1.2 14 -681.782 1394.2 11.25 0.002
## m19 NB(1.177) 1.18 14 -682.802 1396.2 13.29 0.001
## m21 NB(1.1686) 1.17 14 -683.330 1397.3 14.35 0.000
## m17 NB(1.1583) 1.16 14 -683.993 1398.6 15.67 0.000
## Abbreviations:
## family: NB(1.1501) = 'Negative Binomial(1.1501)',
## NB(1.1505) = 'Negative Binomial(1.1505)',
## NB(1.151) = 'Negative Binomial(1.151)',
## NB(1.1511) = 'Negative Binomial(1.1511)',
## NB(1.1524) = 'Negative Binomial(1.1524)',
## NB(1.1583) = 'Negative Binomial(1.1583)',
## NB(1.1619) = 'Negative Binomial(1.1619)',
## NB(1.1633) = 'Negative Binomial(1.1633)',
## NB(1.1686) = 'Negative Binomial(1.1686)',
## NB(1.1759) = 'Negative Binomial(1.1759)',
## NB(1.177) = 'Negative Binomial(1.177)',
## NB(1.1946) = 'Negative Binomial(1.1946)',
## NB(1.1956) = 'Negative Binomial(1.1956)',
## NB(1.2008) = 'Negative Binomial(1.2008)',
## NB(1.2021) = 'Negative Binomial(1.2021)',
## NB(1.2108) = 'Negative Binomial(1.2108)',
## NB(1.2185) = 'Negative Binomial(1.2185)'
## Models ranked by AICc(x)
#original rainfall model (m0) best
bestmodsh<-glm.nb(round(shredder_bio*10)~log(maxvol)+site+log(k.scalar)+I(log(k.scalar)^2), data = fulldata)
fulldata$resid.ff.hydro<-resid(glm.nb(round(shredder_bio*10)~log(maxvol)+site,data=fulldata, na.action=na.exclude))
aic.percent$dhydrotrue[aic.percent$response=="Shredder"]<-Dsquared(glm(resid.ff.hydro~change_mean_temp,family=gaussian, fulldata), adjust=TRUE) #0
aic.percent$dhydrofalse[aic.percent$response=="Shredder"]<-Dsquared(glm(resid.ff.hydro~change_mean_temp,family=gaussian, fulldata), adjust=FALSE) #0
aic.percent$hydrotype[aic.percent$response=="Shredder"]<-"ns"
aic.percent$bestmodel[aic.percent$response=="Shredder"]<-"rain"
#scraper - hydro -------------------
noleakydatatemp<-filter(fulldatatemp, site_brom.id%nin%c("macae_B24", "macae_B22", "macae_B9", "macae_B2", "macae_B11", "macae_B41", "argentina_15"))
aic.hydro.nb(round(fulldatatemp$scraper_bio*10), round(fulldatatemp$scraper_bio*10)~log(fulldatatemp$maxvol)+fulldatatemp$site*(log(fulldatatemp$k.scalar)+I(log(fulldatatemp$k.scalar)^2)), fulldatatemp)
## Warning in model.sel.default(m0, m1, m2, m3, m4, m5, m6, m7, m8, m17,
## m18, : response differs between models
## Model selection table
## (Int) fll$sit log(fll$k.scl) log(fll$k.scl)^2 log(fll$mxv)
## m19 -2.729
## m5 -2.867
## m22 -3.498
## m17 -3.623
## m3 -2.803
## m6 -2.637
## m0 -4.042 + 0.7999 1.07 1.127
## m21 -2.459
## m24 -2.262
## m1 -2.796
## m4 -2.868
## m7 -3.388
## m23 -3.910
## m8 -3.427
## m2 -3.313
## m18 -4.119
## m20 -2.989
## fll$sit:log(fll$k.scl) fll$sit:I(log(fll$k.scl)^2) cv.dpt log(mxv)
## m19 1.0230
## m5 1.0520
## m22 1.2030
## m17 -0.001661 1.1650
## m3 1.0510
## m6 1.0120
## m0 + +
## m21 0.9875
## m24 0.8793
## m1 -0.002630 1.0390
## m4 0.9544
## m7 1.0860
## m23 1.1650
## m8 1.1020
## m2 1.0720
## m18 1.1230
## m20 0.9995
## sit prp.ovr.dys prp.drd.dys men.dpt lng_dry lst_wet chn_cv_tmp
## m19 + -0.8867
## m5 + -0.04577
## m22 + -0.02184
## m17 +
## m3 + -1.2020
## m6 + -0.01133
## m0
## m21 + -0.05129
## m24 +
## m1 +
## m4 + 0.009309
## m7 + 0.1339
## m23 + 0.2950
## m8 +
## m2 + 0.5526
## m18 + 4.7870
## m20 + 0.005044
## chn_men_tmp cv.dpt:sit prp.ovr.dys:sit prp.drd.dys:sit men.dpt:sit
## m19 +
## m5
## m22
## m17 +
## m3
## m6
## m0
## m21
## m24 0.13720
## m1
## m4
## m7
## m23
## m8 0.01191
## m2
## m18 +
## m20 +
## lng_dry:sit lst_wet:sit chn_cv_tmp:sit chn_men_tmp:sit family
## m19 NB(1.5472)
## m5 NB(1.4426)
## m22 + NB(1.5314)
## m17 NB(1.5285)
## m3 NB(1.4254)
## m6 NB(1.4143)
## m0 NB(1.6406)
## m21 + NB(1.4942)
## m24 + NB(1.4753)
## m1 NB(1.3948)
## m4 NB(1.3924)
## m7 NB(1.376)
## m23 + NB(1.4539)
## m8 NB(1.367)
## m2 NB(1.3667)
## m18 NB(1.4329)
## m20 NB(1.4257)
## init.theta df logLik AICc delta weight
## m19 1.55 14 -740.236 1511.1 0.00 0.387
## m5 1.44 9 -746.693 1512.5 1.38 0.194
## m22 1.53 14 -741.373 1513.4 2.27 0.124
## m17 1.53 14 -741.388 1513.4 2.30 0.122
## m3 1.43 9 -747.898 1514.9 3.79 0.058
## m6 1.41 9 -748.577 1516.2 5.15 0.029
## m0 1.64 20 -735.651 1516.8 5.66 0.023
## m21 1.49 14 -743.229 1517.1 5.99 0.019
## m24 1.48 14 -743.658 1517.9 6.84 0.013
## m1 1.39 9 -749.694 1518.5 7.38 0.010
## m4 1.39 9 -749.814 1518.7 7.62 0.009
## m7 1.38 9 -750.471 1520.0 8.94 0.004
## m23 1.45 14 -744.878 1520.4 9.28 0.004
## m8 1.37 9 -751.269 1521.6 10.53 0.002
## m2 1.37 9 -751.371 1521.8 10.73 0.002
## m18 1.43 14 -747.160 1524.9 13.85 0.000
## m20 1.43 14 -747.712 1526.0 14.95 0.000
## Abbreviations:
## family: NB(1.3667) = 'Negative Binomial(1.3667)',
## NB(1.367) = 'Negative Binomial(1.367)',
## NB(1.376) = 'Negative Binomial(1.376)',
## NB(1.3924) = 'Negative Binomial(1.3924)',
## NB(1.3948) = 'Negative Binomial(1.3948)',
## NB(1.4143) = 'Negative Binomial(1.4143)',
## NB(1.4254) = 'Negative Binomial(1.4254)',
## NB(1.4257) = 'Negative Binomial(1.4257)',
## NB(1.4329) = 'Negative Binomial(1.4329)',
## NB(1.4426) = 'Negative Binomial(1.4426)',
## NB(1.4539) = 'Negative Binomial(1.4539)',
## NB(1.4753) = 'Negative Binomial(1.4753)',
## NB(1.4942) = 'Negative Binomial(1.4942)',
## NB(1.5285) = 'Negative Binomial(1.5285)',
## NB(1.5314) = 'Negative Binomial(1.5314)',
## NB(1.5472) = 'Negative Binomial(1.5472)',
## NB(1.6406) = 'Negative Binomial(1.6406)'
## Models ranked by AICc(x)
#m19 m26 m5
aic.hydro.nb(round(noleakydatatemp$scraper_bio*10), round(noleakydatatemp$scraper_bio*10)~log(noleakydatatemp$maxvol)+noleakydatatemp$site*(log(noleakydatatemp$k.scalar)+I(log(noleakydatatemp$k.scalar)^2)), noleakydatatemp)
## Warning in model.sel.default(m0, m1, m2, m3, m4, m5, m6, m7, m8, m17,
## m18, : response differs between models
## Model selection table
## (Int) log(nlk$k.scl) log(nlk$k.scl)^2 log(nlk$mxv) nlk$sit
## m5 -2.677
## m19 -2.851
## m17 -3.644
## m22 -3.662
## m3 -2.883
## m21 -2.579
## m1 -2.442
## m0 -4.173 0.7839 1.088 1.15 +
## m24 -2.324
## m6 -2.838
## m7 -3.440
## m23 -3.978
## m4 -3.010
## m8 -3.487
## m2 -3.383
## m18 -4.242
## m20 -3.038
## log(nlk$k.scl):nlk$sit I(log(nlk$k.scl)^2):nlk$sit cv.dpt log(mxv)
## m5 1.0450
## m19 1.0410
## m17 -0.003657 1.1960
## m22 1.2370
## m3 1.0700
## m21 1.0110
## m1 -0.006364 1.0280
## m0 + +
## m24 0.8937
## m6 1.0410
## m7 1.0980
## m23 1.1800
## m4 0.9899
## m8 1.1150
## m2 1.0900
## m18 1.1490
## m20 1.0250
## sit prp.ovr.dys prp.drd.dys men.dpt lng_dry lst_wet chn_cv_tmp
## m5 + -0.07258
## m19 + -0.8078
## m17 +
## m22 + -0.023510
## m3 + -1.3200
## m21 + -0.05519
## m1 +
## m0
## m24 +
## m6 + -0.009552
## m7 + 0.1410
## m23 + 0.2974
## m4 + 0.007771
## m8 +
## m2 + 0.4518
## m18 + 4.5920
## m20 + 0.002628
## chn_men_tmp cv.dpt:sit prp.ovr.dys:sit prp.drd.dys:sit men.dpt:sit
## m5
## m19 +
## m17 +
## m22
## m3
## m21
## m1
## m0
## m24 0.13810
## m6
## m7
## m23
## m4
## m8 0.01199
## m2
## m18 +
## m20 +
## lng_dry:sit lst_wet:sit chn_cv_tmp:sit chn_men_tmp:sit family
## m5 NB(1.5032)
## m19 NB(1.5702)
## m17 NB(1.5506)
## m22 + NB(1.5405)
## m3 NB(1.4409)
## m21 + NB(1.5255)
## m1 NB(1.4261)
## m0 NB(1.6756)
## m24 + NB(1.5018)
## m6 NB(1.4145)
## m7 NB(1.3962)
## m23 + NB(1.4817)
## m4 NB(1.3973)
## m8 NB(1.3848)
## m2 NB(1.3825)
## m18 NB(1.4557)
## m20 NB(1.4379)
## init.theta df logLik AICc delta weight
## m5 1.5 9 -716.540 1452.2 0.00 0.784
## m19 1.57 14 -712.746 1456.2 4.02 0.105
## m17 1.55 14 -713.841 1458.4 6.21 0.035
## m22 1.54 14 -714.506 1459.8 7.54 0.018
## m3 1.44 9 -720.411 1460.0 7.74 0.016
## m21 1.53 14 -715.135 1461.0 8.80 0.010
## m1 1.43 9 -721.130 1461.4 9.18 0.008
## m0 1.68 20 -707.925 1461.6 9.34 0.007
## m24 1.5 14 -715.686 1462.1 9.90 0.006
## m6 1.41 9 -721.834 1462.8 10.59 0.004
## m7 1.4 9 -722.458 1464.1 11.84 0.002
## m23 1.48 14 -716.752 1464.2 12.03 0.002
## m4 1.4 9 -722.764 1464.7 12.45 0.002
## m8 1.38 9 -723.389 1465.9 13.70 0.001
## m2 1.38 9 -723.604 1466.3 14.13 0.001
## m18 1.46 14 -719.267 1469.3 17.06 0.000
## m20 1.44 14 -720.360 1471.5 19.24 0.000
## Abbreviations:
## family: NB(1.3825) = 'Negative Binomial(1.3825)',
## NB(1.3848) = 'Negative Binomial(1.3848)',
## NB(1.3962) = 'Negative Binomial(1.3962)',
## NB(1.3973) = 'Negative Binomial(1.3973)',
## NB(1.4145) = 'Negative Binomial(1.4145)',
## NB(1.4261) = 'Negative Binomial(1.4261)',
## NB(1.4379) = 'Negative Binomial(1.4379)',
## NB(1.4409) = 'Negative Binomial(1.4409)',
## NB(1.4557) = 'Negative Binomial(1.4557)',
## NB(1.4817) = 'Negative Binomial(1.4817)',
## NB(1.5018) = 'Negative Binomial(1.5018)',
## NB(1.5032) = 'Negative Binomial(1.5032)',
## NB(1.5255) = 'Negative Binomial(1.5255)',
## NB(1.5405) = 'Negative Binomial(1.5405)',
## NB(1.5506) = 'Negative Binomial(1.5506)',
## NB(1.5702) = 'Negative Binomial(1.5702)',
## NB(1.6756) = 'Negative Binomial(1.6756)'
## Models ranked by AICc(x)
#but if remove "leaky" data, then m5 (site+long_dry) is best and only model, still with new leaky
bestmodsc<-glm.nb(round(scraper_bio*10)~log(maxvol)+site*prop.driedout.days, data = fulldata)
secondbestmodsc<-glm.nb(round(scraper_bio*10)~log(maxvol)+site*exposure, data = fulldata)
nextmodsc<-glm.nb(round(scraper_bio*10)~log(maxvol)+site+long_dry, data = fulldata)#preferred as hydro variable more sig, even though aic slightly higher
visreg(bestmodsc, "prop.driedout.days", by="site")#driven especially by puerto rico, a bit macae
visreg(secondbestmodsc, "exposure", by="site")#almost same
visreg(nextmodsc, "long_dry", by="site", ylab="Scraper biomass", overlay=TRUE, partial=FALSE, band=FALSE)
fulldata$resid.sc.hydro<-resid(glm.nb(round(scraper_bio*10)~log(maxvol)+site,data=fulldata, na.action=na.exclude))
aic.percent$dhydrotrue[aic.percent$response=="Scraper"]<-Dsquared(glm(resid.sc.hydro~site*prop.driedout.days,family=gaussian, fulldata), adjust=TRUE) #0.0765
aic.percent$dhydrofalse[aic.percent$response=="Scraper"]<-Dsquared(glm(resid.sc.hydro~site*prop.driedout.days,family=gaussian, fulldata), adjust=FALSE)
aic.percent$hydrotype[aic.percent$response=="Scraper"]<-"contingent"
aic.percent$bestmodel[aic.percent$response=="Scraper"]<-"hydrology"
#gatherer - hydro -------------------
no67185datatemp<-filter(no67185data, mean_temp%nin%NA)%>%filter(cv_mean_temp%nin%NA)%>%
filter(cv.depth%nin%NA)%>%filter(long_dry%nin%NA)%>%filter(last_wet%nin%NA)%>%
filter(prop.overflow.days%nin%NA)%>%filter(prop.driedout.days%nin%NA)
no67185leakydatatemp<-filter(no67185data, mean_temp%nin%NA)%>%filter(cv_mean_temp%nin%NA)%>%
filter(cv.depth%nin%NA)%>%filter(long_dry%nin%NA)%>%filter(last_wet%nin%NA)%>%
filter(prop.overflow.days%nin%NA)%>%filter(prop.driedout.days%nin%NA)%>%
filter(site_brom.id%nin%c("macae_B24", "macae_B22", "macae_B9", "macae_B2", "macae_B11", "macae_B41", "argentina_15"))
aic.hydro.nb(round(no67185datatemp$gatherer_bio*10), round(no67185datatemp$gatherer_bio*10)~log(no67185datatemp$maxvol)+no67185datatemp$site*(I(log(no67185datatemp$k.scalar)^2)), no67185datatemp)
## Warning in model.sel.default(m0, m1, m2, m3, m4, m5, m6, m7, m8, m17,
## m18, : response differs between models
## Model selection table
## (Int) log(n67$k.scl)^2 log(n67$mxv) n67$sit
## m0 -0.6354 -3.624 0.9232 +
## m1 -1.3420
## m6 -2.2010
## m8 -2.7790
## m3 -1.9710
## m7 -2.7520
## m2 -2.4650
## m22 -1.6500
## m5 -2.3510
## m4 -2.3680
## m17 -1.1450
## m19 -1.3900
## m23 -2.8520
## m18 -1.8670
## m24 -2.7170
## m20 -2.6310
## m21 -2.1270
## I(log(n67$k.scl)^2):n67$sit cv.dpt log(mxv) sit prp.ovr.dys
## m0 +
## m1 -0.004932 0.9730 +
## m6 1.1020 +
## m8 1.1510 +
## m3 1.0390 +
## m7 1.1480 +
## m2 1.0890 + 0.6117
## m22 1.1930 +
## m5 1.0940 +
## m4 1.0720 +
## m17 -0.006625 0.9649 +
## m19 1.0010 +
## m23 1.1640 +
## m18 1.0310 + -1.8940
## m24 1.1400 +
## m20 1.0690 +
## m21 1.0460 +
## prp.drd.dys men.dpt lng_dry lst_wet chn_cv_tmp chn_men_tmp
## m0
## m1
## m6 -0.01326
## m8 -0.03115
## m3 -0.6721
## m7 -0.05649
## m2
## m22 -0.06048
## m5 -0.012690
## m4 0.001885
## m17
## m19 -2.1570
## m23 -0.04027
## m18
## m24 -0.02848
## m20 0.010130
## m21 -0.003922
## cv.dpt:sit prp.ovr.dys:sit prp.drd.dys:sit men.dpt:sit lng_dry:sit
## m0
## m1
## m6
## m8
## m3
## m7
## m2
## m22
## m5
## m4
## m17 +
## m19 +
## m23
## m18 +
## m24
## m20 +
## m21 +
## lst_wet:sit chn_cv_tmp:sit chn_men_tmp:sit family init.theta df
## m0 NB(1.0889) 1.09 14
## m1 NB(0.9773) 0.977 9
## m6 NB(0.9751) 0.975 9
## m8 NB(0.9582) 0.958 9
## m3 NB(0.9555) 0.956 9
## m7 NB(0.9501) 0.95 9
## m2 NB(0.9502) 0.95 9
## m22 + NB(1.0135) 1.01 14
## m5 NB(0.9477) 0.948 9
## m4 NB(0.9473) 0.947 9
## m17 NB(0.9924) 0.992 14
## m19 NB(0.9784) 0.978 14
## m23 + NB(0.9633) 0.963 14
## m18 NB(0.9697) 0.97 14
## m24 + NB(0.964) 0.964 14
## m20 NB(0.9541) 0.954 14
## m21 NB(0.9521) 0.952 14
## logLik AICc delta weight
## m0 -712.271 1455.2 0.00 0.890
## m1 -721.075 1461.2 6.07 0.043
## m6 -721.311 1461.7 6.54 0.034
## m8 -722.427 1464.0 8.77 0.011
## m3 -723.070 1465.2 10.06 0.006
## m7 -723.567 1466.2 11.05 0.004
## m2 -723.697 1466.5 11.31 0.003
## m22 -717.940 1466.5 11.34 0.003
## m5 -723.757 1466.6 11.43 0.003
## m4 -723.898 1466.9 11.71 0.003
## m17 -719.472 1469.6 14.40 0.001
## m19 -721.032 1472.7 17.52 0.000
## m23 -721.829 1474.3 19.12 0.000
## m18 -721.853 1474.3 19.16 0.000
## m24 -721.896 1474.4 19.25 0.000
## m20 -722.890 1476.4 21.24 0.000
## m21 -723.004 1476.6 21.47 0.000
## Abbreviations:
## family: NB(0.9473) = 'Negative Binomial(0.9473)',
## NB(0.9477) = 'Negative Binomial(0.9477)',
## NB(0.9501) = 'Negative Binomial(0.9501)',
## NB(0.9502) = 'Negative Binomial(0.9502)',
## NB(0.9521) = 'Negative Binomial(0.9521)',
## NB(0.9541) = 'Negative Binomial(0.9541)',
## NB(0.9555) = 'Negative Binomial(0.9555)',
## NB(0.9582) = 'Negative Binomial(0.9582)',
## NB(0.9633) = 'Negative Binomial(0.9633)',
## NB(0.964) = 'Negative Binomial(0.964)',
## NB(0.9697) = 'Negative Binomial(0.9697)',
## NB(0.9751) = 'Negative Binomial(0.9751)',
## NB(0.9773) = 'Negative Binomial(0.9773)',
## NB(0.9784) = 'Negative Binomial(0.9784)',
## NB(0.9924) = 'Negative Binomial(0.9924)',
## NB(1.0135) = 'Negative Binomial(1.0135)',
## NB(1.0889) = 'Negative Binomial(1.0889)'
## Models ranked by AICc(x)
bestmodga<-glm.nb(round(gatherer_bio*10)~log(maxvol)+site*(I(log(k.scalar)^2)), data = no67185data)
#best is not hydro
aic.hydro.nb(round(no67185leakydatatemp$gatherer_bio*10), round(no67185leakydatatemp$gatherer_bio*10)~log(no67185leakydatatemp$maxvol)+no67185leakydatatemp$site*(I(log(no67185leakydatatemp$k.scalar)^2)), no67185leakydatatemp)
## Warning: glm.fit: algorithm did not converge
## Warning in glm.nb(y ~ log(maxvol) + site * prop.overflow.days, data =
## dataset): alternation limit reached
## Warning in model.sel.default(m0, m1, m2, m3, m4, m5, m6, m7, m8, m17,
## m18, : response differs between models
## Model selection table
## (Int) log(n67$k.scl)^2 log(n67$mxv) n67$sit
## m0 -0.6589 -3.524 0.9272 +
## m6 -2.1870
## m1 -1.1590
## m8 -2.7340
## m3 -1.8780
## m7 -2.6960
## m5 -2.1940
## m2 -2.4270
## m4 -2.3580
## m22 -1.8140
## m17 -1.0710
## m18 -1.7090
## m23 -2.7930
## m24 -2.6810
## m19 -1.6310
## m21 -2.3880
## m20 -2.4370
## I(log(n67$k.scl)^2):n67$sit cv.dpt log(mxv) sit prp.ovr.dys
## m0 +
## m6 1.0990 +
## m1 -0.005867 0.9559 +
## m8 1.1480 +
## m3 1.0300 +
## m7 1.1430 +
## m5 1.0810 +
## m2 1.0900 + 0.5208
## m4 1.0780 +
## m22 1.1870 +
## m17 -0.008332 0.9749 +
## m18 1.0180 + -2.3390
## m23 1.1600 +
## m24 1.1390 +
## m19 1.0270 +
## m21 1.0460 +
## m20 1.0890 +
## prp.drd.dys men.dpt lng_dry lst_wet chn_cv_tmp chn_men_tmp
## m0
## m6 -0.01184
## m1
## m8 -0.03101
## m3 -0.7217
## m7 -0.05333
## m5 -0.02118
## m2
## m4 0.001425
## m22 -0.05150
## m17
## m18
## m23 -0.03682
## m24 -0.02675
## m19 -1.7170
## m21 0.04552
## m20 0.001885
## cv.dpt:sit prp.ovr.dys:sit prp.drd.dys:sit men.dpt:sit lng_dry:sit
## m0
## m6
## m1
## m8
## m3
## m7
## m5
## m2
## m4
## m22
## m17 +
## m18 +
## m23
## m24
## m19 +
## m21 +
## m20 +
## lst_wet:sit chn_cv_tmp:sit chn_men_tmp:sit family init.theta df
## m0 NB(1.0796) 1.08 14
## m6 NB(0.9639) 0.964 9
## m1 NB(0.961) 0.961 9
## m8 NB(0.955) 0.955 9
## m3 NB(0.9513) 0.951 9
## m7 NB(0.9465) 0.946 9
## m5 NB(0.9443) 0.944 9
## m2 NB(0.9461) 0.946 9
## m4 NB(0.9436) 0.944 9
## m22 + NB(0.9901) 0.99 14
## m17 NB(0.9701) 0.97 14
## m18 NB(0.9689) 0.969 14
## m23 + NB(0.9602) 0.96 14
## m24 + NB(0.9582) 0.958 14
## m19 NB(0.9611) 0.961 14
## m21 NB(0.9528) 0.953 14
## m20 NB(0.9506) 0.951 14
## logLik AICc delta weight
## m0 -680.639 1392.0 0.00 0.859
## m6 -689.513 1398.2 6.13 0.040
## m1 -689.865 1398.9 6.84 0.028
## m8 -689.895 1398.9 6.90 0.027
## m3 -690.614 1400.4 8.33 0.013
## m7 -691.052 1401.2 9.21 0.009
## m5 -691.125 1401.4 9.36 0.008
## m2 -691.204 1401.6 9.51 0.007
## m4 -691.365 1401.9 9.84 0.006
## m22 -687.375 1405.5 13.47 0.001
## m17 -688.819 1408.4 16.36 0.000
## m18 -689.161 1409.1 17.05 0.000
## m23 -689.309 1409.4 17.34 0.000
## m24 -689.609 1410.0 17.94 0.000
## m19 -689.811 1410.4 18.35 0.000
## m21 -690.125 1411.0 18.97 0.000
## m20 -690.391 1411.5 19.50 0.000
## Abbreviations:
## family: NB(0.9436) = 'Negative Binomial(0.9436)',
## NB(0.9443) = 'Negative Binomial(0.9443)',
## NB(0.9461) = 'Negative Binomial(0.9461)',
## NB(0.9465) = 'Negative Binomial(0.9465)',
## NB(0.9506) = 'Negative Binomial(0.9506)',
## NB(0.9513) = 'Negative Binomial(0.9513)',
## NB(0.9528) = 'Negative Binomial(0.9528)',
## NB(0.955) = 'Negative Binomial(0.955)',
## NB(0.9582) = 'Negative Binomial(0.9582)',
## NB(0.9602) = 'Negative Binomial(0.9602)',
## NB(0.961) = 'Negative Binomial(0.961)',
## NB(0.9611) = 'Negative Binomial(0.9611)',
## NB(0.9639) = 'Negative Binomial(0.9639)',
## NB(0.9689) = 'Negative Binomial(0.9689)',
## NB(0.9701) = 'Negative Binomial(0.9701)',
## NB(0.9901) = 'Negative Binomial(0.9901)',
## NB(1.0796) = 'Negative Binomial(1.0796)'
## Models ranked by AICc(x)
#even with leaky brom removed, best is not hydro
no67185data$resid.ga.hydro<-resid(glm.nb(round(gatherer_bio*10)~log(maxvol)+site,data=no67185data, na.action=na.exclude))
aic.percent$dhydrotrue[aic.percent$response=="Gatherer"]<-Dsquared(glm(resid.ga.hydro~site*prop.overflow.days,family=gaussian, no67185data), adjust=TRUE)
aic.percent$dhydrofalse[aic.percent$response=="Gatherer"]<-Dsquared(glm(resid.ga.hydro~site*prop.overflow.days,family=gaussian, no67185data), adjust=FALSE)
aic.percent$hydrotype[aic.percent$response=="Gatherer"]<-"ns"
aic.percent$bestmodel[aic.percent$response=="Gatherer"]<-"rain"
#engulfer - hydro ------------------
noargco13datatemp<-filter(noargco13data, mean_temp%nin%NA)%>%filter(cv_mean_temp%nin%NA)%>%
filter(cv.depth%nin%NA)%>%filter(long_dry%nin%NA)%>%filter(last_wet%nin%NA)%>%
filter(prop.overflow.days%nin%NA)%>%filter(prop.driedout.days%nin%NA)
aic.hydro.nb(round(noargco13datatemp$engulfer_bio*100), round(noargco13datatemp$engulfer_bio*100)~log(noargco13datatemp$maxvol)+noargco13datatemp$site, noargco13datatemp)
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning in glm.nb(y ~ log(maxvol) + site * mean.depth, data = dataset):
## alternation limit reached
## Warning in model.sel.default(m0, m1, m2, m3, m4, m5, m6, m7, m8, m17,
## m18, : response differs between models
## Model selection table
## (Int) log(n13$mxv) n13$sit cv.dpt log(mxv) sit prp.ovr.dys
## m0 2.439 0.3279 +
## m4 5.549 -0.32040 +
## m8 3.766 0.07157 +
## m24 3.968 0.03027 +
## m3 3.357 0.16720 +
## m2 2.858 0.22030 + 0.6950
## m1 3.426 -0.002548 0.16460 +
## m7 2.790 0.26070 +
## m6 2.289 0.35260 +
## m5 2.589 0.29950 +
## m23 3.061 0.20380 +
## m21 3.538 0.13630 +
## m19 2.700 0.33000 +
## m17 5.219 -0.005816 -0.14530 +
## m18 3.975 0.01292 + 0.4798
## m22 2.391 0.35370 +
## m20 11.020 -1.30000 +
## prp.drd.dys men.dpt lng_dry lst_wet chn_cv_tmp chn_men_tmp
## m0
## m4 0.013170
## m8 -0.03349
## m24 -1.09700
## m3 -0.5562
## m2
## m1
## m7 -0.072
## m6 0.001683
## m5 -0.002128
## m23 1.424
## m21 -0.080760
## m19 -1.6410
## m17
## m18
## m22 -0.005860
## m20 -0.007263
## cv.dpt:sit prp.ovr.dys:sit prp.drd.dys:sit men.dpt:sit lng_dry:sit
## m0
## m4
## m8
## m24
## m3
## m2
## m1
## m7
## m6
## m5
## m23
## m21 +
## m19 +
## m17 +
## m18 +
## m22
## m20 +
## lst_wet:sit chn_cv_tmp:sit chn_men_tmp:sit family init.theta df
## m0 NB(0.3398) 0.34 6
## m4 NB(0.3454) 0.345 7
## m8 NB(0.3443) 0.344 7
## m24 + NB(0.3649) 0.365 10
## m3 NB(0.3406) 0.341 7
## m2 NB(0.3404) 0.34 7
## m1 NB(0.3404) 0.34 7
## m7 NB(0.3399) 0.34 7
## m6 NB(0.3399) 0.34 7
## m5 NB(0.3398) 0.34 7
## m23 + NB(0.3512) 0.351 10
## m21 NB(0.3427) 0.343 10
## m19 NB(0.3427) 0.343 10
## m17 NB(0.3426) 0.343 10
## m18 NB(0.3426) 0.343 10
## m22 + NB(0.3415) 0.341 10
## m20 NB(0.3408) 0.336 10
## logLik AICc delta weight
## m0 -502.897 1018.6 0.00 0.195
## m4 -501.995 1019.0 0.46 0.155
## m8 -502.171 1019.4 0.81 0.130
## m24 -499.014 1020.1 1.55 0.090
## m3 -502.771 1020.6 2.01 0.071
## m2 -502.797 1020.6 2.07 0.070
## m1 -502.798 1020.6 2.07 0.069
## m7 -502.870 1020.8 2.21 0.065
## m6 -502.887 1020.8 2.24 0.064
## m5 -502.893 1020.8 2.26 0.063
## m23 -501.064 1024.2 5.65 0.012
## m21 -502.408 1026.9 8.33 0.003
## m19 -502.427 1026.9 8.37 0.003
## m17 -502.433 1026.9 8.39 0.003
## m18 -502.445 1027.0 8.41 0.003
## m22 -502.626 1027.3 8.77 0.002
## m20 -502.731 1027.5 8.98 0.002
## Abbreviations:
## family: NB(0.3398) = 'Negative Binomial(0.3398)',
## NB(0.3399) = 'Negative Binomial(0.3399)',
## NB(0.3404) = 'Negative Binomial(0.3404)',
## NB(0.3406) = 'Negative Binomial(0.3406)',
## NB(0.3408) = 'Negative Binomial(0.3408)',
## NB(0.3415) = 'Negative Binomial(0.3415)',
## NB(0.3426) = 'Negative Binomial(0.3426)',
## NB(0.3427) = 'Negative Binomial(0.3427)',
## NB(0.3443) = 'Negative Binomial(0.3443)',
## NB(0.3454) = 'Negative Binomial(0.3454)',
## NB(0.3512) = 'Negative Binomial(0.3512)',
## NB(0.3649) = 'Negative Binomial(0.3649)'
## Models ranked by AICc(x)
#m0 m7 the cv mean temp mode doesnt have any sig terms, so stick with null
bestmoden<-glm.nb(round(engulfer_bio*100)~log(maxvol)+site, data = noargco13datatemp)
noargco13data$resid.en.hydro<-resid(glm.nb(round(noargco13data$engulfer_bio*100)~log(maxvol)+site,data=noargco13data, na.action=na.exclude))
aic.percent$dhydrotrue[aic.percent$response=="Engulfer"]<-Dsquared(glm(resid.en.hydro~change_cv_temp,family=gaussian, noargco13data), adjust=TRUE) #0
aic.percent$dhydrofalse[aic.percent$response=="Engulfer"]<-Dsquared(glm(resid.en.hydro~change_cv_temp,family=gaussian, noargco13data), adjust=FALSE) #0
aic.percent$hydrotype[aic.percent$response=="Engulfer"]<-"ns"
aic.percent$bestmodel[aic.percent$response=="Engulfer"]<-"neither"
#piercer-hydro -----------------
nococrprdatatemp<-filter(nococrprdata, mean_temp%nin%NA)%>%filter(cv_mean_temp%nin%NA)%>%
filter(cv.depth%nin%NA)%>%filter(long_dry%nin%NA)%>%filter(last_wet%nin%NA)%>%
filter(prop.overflow.days%nin%NA)%>%filter(prop.driedout.days%nin%NA)
nococrprleakydatatemp<-filter(nococrprdata, mean_temp%nin%NA)%>%filter(cv_mean_temp%nin%NA)%>%
filter(cv.depth%nin%NA)%>%filter(long_dry%nin%NA)%>%filter(last_wet%nin%NA)%>%
filter(prop.overflow.days%nin%NA)%>%filter(prop.driedout.days%nin%NA)%>%
filter(site_brom.id%nin%c("macae_B24", "macae_B22", "macae_B9", "macae_B2", "macae_B11", "macae_B41", "argentina_15"))
aic.hydro.nb(round(nococrprdatatemp$piercer_bio*10), round(nococrprdatatemp$piercer_bio*10)~log(nococrprdatatemp$maxvol)+nococrprdatatemp$site*(log(nococrprdatatemp$k.scalar)), nococrprdatatemp)
## Error in while ((it <- it + 1) < limit && abs(del) > eps) {: missing value where TRUE/FALSE needed
#m18 m6 driven by macae, but only 3 sites..m18 contingent whereas m6 general
aic.hydro.nb(round(nococrprleakydatatemp$piercer_bio*10), round(nococrprleakydatatemp$piercer_bio*10)~log(nococrprleakydatatemp$maxvol)+nococrprleakydatatemp$site*(log(nococrprleakydatatemp$k.scalar)), nococrprleakydatatemp)
## Error in while ((it <- it + 1) < limit && abs(del) > eps) {: missing value where TRUE/FALSE needed
#still m18 m6
bestmodpi<-glm.nb(round(piercer_bio*10)~log(maxvol)+site*prop.overflow.days, data = nococrprdata)
## Error in while ((it <- it + 1) < limit && abs(del) > eps) {: missing value where TRUE/FALSE needed
visreg(bestmodpi, "prop.overflow.days", by="site",ylab="Piercer biomass", overlay=TRUE, partial=FALSE, band=FALSE)
## Error in setupF(fit, xvar, parent.frame()): object 'bestmodpi' not found
nococrprdata$resid.pi.hydro<-resid(glm.nb(round(piercer_bio*10)~log(maxvol)+site,data=nococrprdata, na.action=na.exclude))
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
aic.percent$dhydrotrue[aic.percent$response=="Piercer"]<-Dsquared(glm(resid.pi.hydro~site*prop.overflow.days,family=gaussian, nococrprdata), adjust=TRUE) #0.0742
aic.percent$dhydrofalse[aic.percent$response=="Piercer"]<-Dsquared(glm(resid.pi.hydro~site*prop.overflow.days,family=gaussian, nococrprdata), adjust=FALSE)
aic.percent$hydrotype[aic.percent$response=="Piercer"]<-"contingent"
aic.percent$bestmodel[aic.percent$response=="Piercer"]<-"hydrology"
#bacteria - hydro ---------------
noargco123datatemp<-filter(noargco123data, mean_temp%nin%NA)%>%filter(cv_mean_temp%nin%NA)%>%
filter(cv.depth%nin%NA)%>%filter(long_dry%nin%NA)%>%filter(last_wet%nin%NA)%>%
filter(prop.overflow.days%nin%NA)%>%filter(prop.driedout.days%nin%NA)
aic.hydro.nb(round(noargco123datatemp$bacteria.per.nl.final*100), round(noargco123datatemp$bacteria.per.nl.final*100)~log(noargco123datatemp$maxvol)+noargco123datatemp$site+(log(noargco123datatemp$mu.scalar)), noargco123datatemp)
## Warning in model.sel.default(m0, m1, m2, m3, m4, m5, m6, m7, m8, m17,
## m18, : response differs between models
## Model selection table
## (Int) log(n12$mxv) log(n12$mu.scl) n12$sit cv.dpt log(mxv) sit
## m0 2.341 0.6261 -0.1116 +
## m6 1.869 0.7026 +
## m7 2.338 0.6307 +
## m4 1.843 0.7425 +
## m22 1.414 0.7805 +
## m2 2.223 0.6381 +
## m3 1.952 0.6959 +
## m19 1.120 0.8551 +
## m5 2.161 0.6651 +
## m8 2.215 0.6555 +
## m1 2.217 -5.830e-05 0.6559 +
## m21 1.990 0.7249 +
## m20 1.207 0.8380 +
## m17 1.430 -1.851e-05 0.8064 +
## m18 2.074 0.6359 +
## m23 2.427 0.6123 +
## m24 2.268 0.6452 +
## prp.ovr.dys prp.drd.dys men.dpt lng_dry lst_wet chn_cv_tmp
## m0
## m6 0.006008
## m7 -0.09087
## m4 -0.004097
## m22 0.009165
## m2 0.378
## m3 0.2459
## m19 0.2614
## m5 0.003421
## m8
## m1
## m21 -0.098440
## m20 0.002987
## m17
## m18 1.159
## m23 -0.32600
## m24
## chn_men_tmp cv.dpt:sit prp.ovr.dys:sit prp.drd.dys:sit men.dpt:sit
## m0
## m6
## m7
## m4
## m22
## m2
## m3
## m19 +
## m5
## m8 -0.001314
## m1
## m21
## m20 +
## m17 +
## m18 +
## m23
## m24 -0.588400
## lng_dry:sit lst_wet:sit chn_cv_tmp:sit chn_men_tmp:sit family
## m0 NB(2.5895)
## m6 NB(2.5519)
## m7 NB(2.5246)
## m4 NB(2.5229)
## m22 + NB(2.6634)
## m2 NB(2.5169)
## m3 NB(2.5118)
## m19 NB(2.6554)
## m5 NB(2.5052)
## m8 NB(2.5049)
## m1 NB(2.5046)
## m21 + NB(2.6412)
## m20 NB(2.6003)
## m17 NB(2.5869)
## m18 NB(2.5539)
## m23 + NB(2.5507)
## m24 + NB(2.5327)
## init.theta df logLik AICc delta weight
## m0 2.59 7 -868.355 1751.8 0.00 0.334
## m6 2.55 7 -869.267 1753.6 1.82 0.134
## m7 2.52 7 -869.952 1755.0 3.19 0.068
## m4 2.52 7 -869.995 1755.1 3.28 0.065
## m22 2.66 10 -866.576 1755.3 3.53 0.057
## m2 2.52 7 -870.142 1755.4 3.57 0.056
## m3 2.51 7 -870.274 1755.6 3.84 0.049
## m19 2.66 10 -866.768 1755.7 3.92 0.047
## m5 2.51 7 -870.439 1755.9 4.17 0.042
## m8 2.5 7 -870.448 1756.0 4.19 0.041
## m1 2.5 7 -870.457 1756.0 4.20 0.041
## m21 2.64 10 -867.103 1756.4 4.59 0.034
## m20 2.6 10 -868.088 1758.3 6.56 0.013
## m17 2.59 10 -868.412 1759.0 7.21 0.009
## m18 2.55 10 -869.223 1760.6 8.83 0.004
## m23 2.55 10 -869.300 1760.8 8.98 0.004
## m24 2.53 10 -869.749 1761.7 9.88 0.002
## Abbreviations:
## family: NB(2.5046) = 'Negative Binomial(2.5046)',
## NB(2.5049) = 'Negative Binomial(2.5049)',
## NB(2.5052) = 'Negative Binomial(2.5052)',
## NB(2.5118) = 'Negative Binomial(2.5118)',
## NB(2.5169) = 'Negative Binomial(2.5169)',
## NB(2.5229) = 'Negative Binomial(2.5229)',
## NB(2.5246) = 'Negative Binomial(2.5246)',
## NB(2.5327) = 'Negative Binomial(2.5327)',
## NB(2.5507) = 'Negative Binomial(2.5507)',
## NB(2.5519) = 'Negative Binomial(2.5519)',
## NB(2.5539) = 'Negative Binomial(2.5539)',
## NB(2.5869) = 'Negative Binomial(2.5869)',
## NB(2.5895) = 'Negative Binomial(2.5895)',
## NB(2.6003) = 'Negative Binomial(2.6003)',
## NB(2.6412) = 'Negative Binomial(2.6412)',
## NB(2.6554) = 'Negative Binomial(2.6554)',
## NB(2.6634) = 'Negative Binomial(2.6634)'
## Models ranked by AICc(x)
#m7 m0 m6 m2 m19 m5 m1...agian, m7 seems like a spurious model driven by between site differences, nor is term sig
bestmodba<-glm.nb(round(bacteria.per.nl.final*100)~log(maxvol)+site+log(mu.scalar), data = noargco123data)
noargco123data$resid.ba.hydro<-resid(glm.nb(round(piercer_bio*10)~log(maxvol)+site,data=noargco123data, na.action=na.exclude))
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
aic.percent$dhydrotrue[aic.percent$response=="Bacterial density"]<-Dsquared(glm(resid.ba.hydro~change_cv_temp,family=gaussian, noargco123data), adjust=TRUE) #0.0742
aic.percent$dhydrofalse[aic.percent$response=="Bacterial density"]<-Dsquared(glm(resid.ba.hydro~change_cv_temp,family=gaussian, noargco123data), adjust=FALSE) #0.0742
aic.percent$hydrotype[aic.percent$response=="Bacterial density"]<-"ns"
aic.percent$bestmodel[aic.percent$response=="Bacterial density"]<-"rain"
#totalbio - hydro
aic.hydro.nb(round(fulldatatemp$totalbio*10), round(fulldatatemp$totalbio*10)~log(fulldatatemp$maxvol)+fulldatatemp$site, fulldatatemp)
## Warning in model.sel.default(m0, m1, m2, m3, m4, m5, m6, m7, m8, m17,
## m18, : response differs between models
## Model selection table
## (Int) fll$sit log(fll$mxv) cv.dpt log(mxv) sit prp.ovr.dys
## m1 -0.02467 -0.002696 0.9052 +
## m0 -0.56230 + 0.9578
## m3 -0.37090 0.9375 +
## m6 -0.44840 0.9507 +
## m17 0.45060 -0.004175 0.8474 +
## m8 -0.56360 0.9579 +
## m7 -0.53560 0.9531 +
## m2 -0.54870 0.9532 + 0.1373
## m5 -0.58690 0.9600 +
## m4 -0.56870 0.9595 +
## m18 -0.25620 0.9559 + -2.8090
## m20 0.12080 0.8322 +
## m19 0.00862 0.8931 +
## m21 -0.17590 0.9056 +
## m24 -0.27010 0.9071 +
## m22 -0.40020 0.9462 +
## m23 -0.69840 0.9812 +
## prp.drd.dys men.dpt lng_dry lst_wet chn_cv_tmp chn_men_tmp
## m1
## m0
## m3 -0.2905
## m6 -0.002930
## m17
## m8 0.005573
## m7 0.01454
## m2
## m5 0.002037
## m4 -9.512e-05
## m18
## m20 1.305e-03
## m19 -0.7506
## m21 -0.013680
## m24 0.009137
## m22 -0.003821
## m23 0.01516
## cv.dpt:sit prp.ovr.dys:sit prp.drd.dys:sit men.dpt:sit lng_dry:sit
## m1
## m0
## m3
## m6
## m17 +
## m8
## m7
## m2
## m5
## m4
## m18 +
## m20 +
## m19 +
## m21 +
## m24
## m22
## m23
## lst_wet:sit chn_cv_tmp:sit chn_men_tmp:sit family init.theta df
## m1 NB(2.502) 2.5 9
## m0 NB(2.4423) 2.44 8
## m3 NB(2.4541) 2.45 9
## m6 NB(2.4519) 2.45 9
## m17 NB(2.5971) 2.6 14
## m8 NB(2.4473) 2.45 9
## m7 NB(2.4438) 2.44 9
## m2 NB(2.4434) 2.44 9
## m5 NB(2.4429) 2.44 9
## m4 NB(2.4423) 2.44 9
## m18 NB(2.5789) 2.58 14
## m20 NB(2.5274) 2.53 14
## m19 NB(2.5206) 2.52 14
## m21 NB(2.5042) 2.5 14
## m24 + NB(2.5043) 2.5 14
## m22 + NB(2.504) 2.5 14
## m23 + NB(2.4827) 2.48 14
## logLik AICc delta weight
## m1 -1013.172 2045.4 0.00 0.449
## m0 -1015.566 2048.0 2.57 0.124
## m3 -1015.101 2049.3 3.86 0.065
## m6 -1015.195 2049.5 4.05 0.059
## m17 -1009.550 2049.7 4.29 0.052
## m8 -1015.371 2049.8 4.40 0.050
## m7 -1015.510 2050.1 4.68 0.043
## m2 -1015.526 2050.1 4.71 0.043
## m5 -1015.543 2050.2 4.74 0.042
## m4 -1015.566 2050.2 4.79 0.041
## m18 -1010.463 2051.6 6.12 0.021
## m20 -1012.324 2055.3 9.84 0.003
## m19 -1012.574 2055.8 10.34 0.003
## m21 -1013.181 2057.0 11.55 0.001
## m24 -1013.208 2057.0 11.61 0.001
## m22 -1013.212 2057.0 11.62 0.001
## m23 -1013.984 2058.6 13.16 0.001
## Abbreviations:
## family: NB(2.4423) = 'Negative Binomial(2.4423)',
## NB(2.4429) = 'Negative Binomial(2.4429)',
## NB(2.4434) = 'Negative Binomial(2.4434)',
## NB(2.4438) = 'Negative Binomial(2.4438)',
## NB(2.4473) = 'Negative Binomial(2.4473)',
## NB(2.4519) = 'Negative Binomial(2.4519)',
## NB(2.4541) = 'Negative Binomial(2.4541)',
## NB(2.4827) = 'Negative Binomial(2.4827)',
## NB(2.502) = 'Negative Binomial(2.502)',
## NB(2.504) = 'Negative Binomial(2.504)',
## NB(2.5042) = 'Negative Binomial(2.5042)',
## NB(2.5043) = 'Negative Binomial(2.5043)',
## NB(2.5206) = 'Negative Binomial(2.5206)',
## NB(2.5274) = 'Negative Binomial(2.5274)',
## NB(2.5789) = 'Negative Binomial(2.5789)',
## NB(2.5971) = 'Negative Binomial(2.5971)'
## Models ranked by AICc(x)
besttb<-glm.nb(round(totalbio*10)~log(maxvol)+site, data = fulldata)
#m1, but this seems spurious, driven by one extreme value in argentina (if remove this site effect lost), so accept null
bestmodtb<-glm.nb(round(totalbio*10)~log(maxvol)+site, data = fulldata)
fulldata$resid.tb.hydro<-resid(glm.nb(round(totalbio*10)~log(maxvol)+site,data=fulldata, na.action=na.exclude))
aic.percent$dhydrotrue[aic.percent$response=="Total Invertebrates"]<-Dsquared(glm(resid.tb.hydro~cv.depth,family=gaussian, fulldata), adjust=TRUE) #0
aic.percent$dhydrofalse[aic.percent$response=="Total Invertebrates"]<-Dsquared(glm(resid.tb.hydro~cv.depth,family=gaussian, fulldata), adjust=FALSE)
aic.percent$hydrotype[aic.percent$response=="Total Invertebrates"]<-"general"
aic.percent$bestmodel[aic.percent$response=="Total Invertebrates"]<-"hydrology"
#overall figure
dodge <- position_dodge(width = 0.9)
aic.percent$response <- factor(aic.percent$response, levels = c(response<-as.vector(c("CO2 flux","Decomposition", "Nitrogen uptake", "Bacterial density", "Total Invertebrates","Engulfer","Shredder", "Piercer","Gatherer","Scraper", "Filter feeder"))))
ggplot(data = aic.percent, aes(x = response, y = draintrue))+
geom_bar(data=aic.percent, aes(y=draintrue,x=response, fill=raintype), stat="identity", width = 0.75)+
labs(title = "Effect of rainfall on ecosystem responses",
y = "Proportion residual deviance explained by rainfall (adj. for number of parameters)", x = "Response type")+
theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5))
ggplot(data = aic.percent, aes(x = response, y = dhydrotrue))+
geom_bar(data=aic.percent, aes(y=dhydrotrue,x=response, fill=hydrotype), stat="identity", width = 0.75)+
labs(title = "Effect of hydrology on ecosystem responses",
y = "Proportion residual deviance explained by hydrology (adj. for number of parameters)", x = "Response type")+
theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5))
#===concordance
library(Agreement)
preds<-c(12,2,3,5,4,5,6,7,3)
a<-c(2,5,3,1,7,8,11,12)
obs<-log(round(a)+0.001)
preds<-rnorm(30)
obs<-rnorm(30)
xx<-as.vector(predobs$preds)
## Error in as.vector(predobs$preds): object 'predobs' not found
yy<-as.vector(predobs$obs)
## Error in as.vector(predobs$obs): object 'predobs' not found
sc.agr<-agreement(x=xx, y=yy, error="const",TDI_a = 1, target="random")
## Error in agreement(x = xx, y = yy, error = "const", TDI_a = 1, target = "random"): object 'yy' not found
kendall.global(predobs)
## Error in as.matrix(Y): object 'predobs' not found
W<-kendall.global(predobs)
## Error in as.matrix(Y): object 'predobs' not found
Sp<-cor(predobs, use="pairwise.complete.obs", method="spearman")
## Error in is.data.frame(x): object 'predobs' not found
fulldata$scaled.n15.bromeliad.final<-(fulldata$n15.bromeliad.final+4)^0.125
fulldata$sqrt.decomp<-sqrt(fulldata$decomp)
fulldata$log.co2.final<-log(fulldata$co2.final)
singlesite.concord.hydro("filter.feeder_bio", fulldata, "puertorico", "argentina", 100, 2, "gaussian")
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m4 1.5710 -0.4340 0.04978
## m1 -0.4805 -0.01073 0.3112
## m3 -0.6190 0.2890 -2.024
## m2 -2.1120 0.3869 3.159
## m5 -1.3330 0.3847 -0.02993
## m6 -1.5860 0.4440
## lst_wet df logLik AICc delta weight
## m4 4 -39.890 89.4 0.00 0.996
## m1 4 -46.572 102.7 13.36 0.001
## m3 4 -46.823 103.2 13.87 0.001
## m2 4 -47.434 104.5 15.09 0.001
## m5 4 -47.585 104.8 15.39 0.000
## m6 -0.01986 4 -47.650 104.9 15.52 0.000
## Models ranked by AICc(x)
## a reference target
## [1,] "filter.feeder_bio" "puertorico" "argentina" "hydro" "1"
##
## [1,] "0.143837846721124" "0.516703571149646" "0.278375948517425"
##
## [1,] "0.779055762977237" "0.560031835939925"
sites<-c("puertorico","argentina", "macae", "frenchguiana")
multisite.concord.hydro("scaled.n15.bromeliad.final", fulldata, sites, "argentina", 100, 2, "gaussian")
## Model selection table
## (Int) log(mxv) sit cv.dpt prp.ovr.dys prp.drd.dys men.dpt
## m0 1.269 0.003700 +
## m6 1.242 0.007945 +
## m4 1.226 0.016090 + -0.0004974
## m1 1.251 0.006461 + 0.0001149
## m5 1.256 0.006001 +
## m2 1.272 0.002473 + 0.0119
## m3 1.270 0.003484 + -0.00237
## lng_dry lst_wet df logLik AICc delta weight
## m0 5 75.575 -140.4 0.00 0.313
## m6 0.0006775 6 75.993 -139.0 1.46 0.151
## m4 6 75.844 -138.7 1.76 0.130
## m1 6 75.626 -138.2 2.20 0.104
## m5 0.0005263 6 75.623 -138.2 2.20 0.104
## m2 6 75.582 -138.2 2.28 0.100
## m3 6 75.575 -138.1 2.30 0.099
## Models ranked by AICc(x)
## a target nsites
## [1,] "scaled.n15.bromeliad.final" "fulldata" "argentina" "hydro" "3"
##
## [1,] "NA" "NA" "NA" "NA" "NA" "NA"
singlesite.concord<-function(a, dataset, reference, target, scalar, deltalimit, family)
{
b<-filter(dataset, site%in%reference)
c<-filter(dataset, site%in%target)
summ<-cbind(a, reference, target, "rain", 1 ,"NA", "NA", "NA", "NA", "NA", "NA")
try({
if (family=="nb") {
correction<-mean(log(round(c[,a]*scalar)+0.001))-mean(log(round(b[,a]*scalar)+0.001))
obs<-log(round(c[,a]*scalar)+0.001)
nbset<-aic.sitenb(round(b[,a]*scalar), b) #set of nb models
newtheta<-as.numeric(as.vector(levels(nbset[1]$init.theta))[as.numeric(nbset[1]$init.theta)]) #what is theta of best nb model? theta differs between nb models so can't average
allsc<-aic.site(round(b[,a]*scalar),negative.binomial(theta=newtheta), b) #rerun AIC with fixed theta
}
else{
correction<-mean(c[,a])-mean(b[,a])
obs<-c[,a]
allsc<-aic.site((b[,a]),gaussian, b)
}
sc.ests<-get.models(allsc, subset= delta == 0); preds<- as.vector(sapply(sc.ests, predict, newdata = c))+correction#default to top model if only one
try({
sc.ests<-model.avg(allsc, subset= delta < deltalimit, revised.var = TRUE, fit = TRUE)#try because wont run if only one model within deltalimit
preds<-as.vector(predict(sc.ests,c, full = TRUE))+correction
})
sc.agr<-agreement(x=preds, y=obs, error="const",TDI_a = 1, target="random") #tdi is bogus, just to get CCC output
predobs<-as.matrix(cbind(preds,obs))
W<-kendall.global(predobs)
Sp<-cor(predobs, use="pairwise.complete.obs", method="spearman")
summ<-cbind(a, reference, target, "rain", 1 ,sc.agr$Estimate$CCC[1], sc.agr$Estimate$Precision[1], sc.agr$Estimate$Accuracy[1], W$Concordance_analysis[1], Sp[2])
})
return(summ)
}
singlesite.concord.hydro<-function(a, dataset, reference, target, scalar, deltalimit, family)
{
b<-filter(dataset, site%in%reference)
c<-filter(dataset, site%in%target)
summ<-cbind(a, reference, target, "hydro", 1 ,"NA", "NA", "NA", "NA", "NA", "NA")
try({
if (family=="nb") {
correction<-mean(log(c[,a]*scalar+0.001))-mean(log(b[,a]*scalar+0.001))
obs<-log(round(c[,a]*scalar)+0.001)
nbset<-aic.site.hydro.nb(round(b[,a]*scalar), b) #set of nb models
newtheta<-as.numeric(as.vector(levels(nbset[1]$init.theta))[as.numeric(nbset[1]$init.theta)]) #what is theta of best nb model? theta differs between nb models so can't average
allsc<-aic.site.hydro(round(b[,a]*scalar),negative.binomial(theta=newtheta), b) #rerun AIC with fixed theta
}
else{
correction<-mean(c[,a])-mean(b[,a])
obs<-c[,a]
allsc<-aic.site.hydro(b[,a],gaussian, b)
}
sc.ests<-get.models(allsc, subset= delta == 0); preds<- as.vector(sapply(sc.ests, predict, newdata = c))+correction#default to top model if only one
try({
sc.ests<-model.avg(allsc, subset= delta < deltalimit, revised.var = TRUE, fit = TRUE)#try because wont run if only one model within deltalimit
preds<-as.vector(predict(sc.ests,c, full = TRUE))+correction
})
sc.agr<-agreement(x=preds, y=log(round(c[,a]*scalar)+0.001), error="const",TDI_a = 1, target="random") #tdi is bogus, just to get CCC output
predobs<-as.matrix(cbind(preds, log(round(c[,a]*scalar)+0.001)))
W<-kendall.global(predobs)
Sp<-cor(predobs, use="pairwise.complete.obs", method="spearman")
summ<-cbind(a, reference, target, "hydro", 1 ,sc.agr$Estimate$CCC[1], sc.agr$Estimate$Precision[1], sc.agr$Estimate$Accuracy[1], W$Concordance_analysis[1], Sp[2])
})
return(summ)
}
multisite.concord<-function(a, dataset, sites, target, scalar, deltalimit, family) #a is variable in quotes, target is site name in quotes
{
b<-filter(dataset, site%in%sites)%>%filter(site%nin%target)
c<-filter(dataset, site%in%target)
nsites<-nrow(distinct(select(b, site)))
predmatrix<-cbind(c["maxvol"], c["k.scalar"],c["mu.scalar"])
predmatrix[,a]<-c[,a]
nr<-nrow(c)
predmatrix$site<-rep(b[,"site"][1], nr)
summ<-cbind(a, deparse(substitute(dataset)), target, "rain", nsites ,"NA", "NA", "NA", "NA", "NA", "NA")
try({
if (family=="nb")
{
correction<-mean(log(c[,a]*scalar+0.001))-mean(log(b[1:nr,a]*scalar+0.001))
obs<-log(round(c[,a]*scalar)+0.001)
nbset<-aic.lmxnb.add(round(b[,a]*scalar), b) #set of nb models
newtheta<-as.numeric(as.vector(levels(nbset[1]$init.theta))[as.numeric(nbset[1]$init.theta)])
allsc<-aic.lmx.add(round(b[,a]*scalar),negative.binomial(theta=newtheta), b) #rerun AIC with fixed theta
}
else
{
correction<-mean(c[,a])-mean(b[1:nr,a])
obs<-c[,a]
allsc<-aic.lmx.add(b[,a],gaussian, b)
}
sc.ests<-get.models(allsc, subset= delta == 0)
preds<- as.vector(sapply(sc.ests, predict, newdata = predmatrix))
try({
sc.ests<-model.avg(allsc, subset= delta < deltalimit, revised.var = TRUE, fit = TRUE)#now get average parameter estimates from plausible set
preds<-as.vector(predict(sc.ests,predmatrix, full = TRUE))+correction
})
predobs<-as.matrix(cbind(preds, obs))
sc.agr$Estimate$CCC[1]<-"NA"
sc.agr$Estimate$Precision[1]<- cor(predobs, use="pairwise.complete.obs", method="pearson")[2]
sc.agr$Estimate$Accuracy[1]<-"NA"
try({sc.agr<-agreement(x=preds, y=obs, error="const",TDI_a = 1, target="random")
})
W<-kendall.global(predobs)
Sp<-cor(predobs, use="pairwise.complete.obs", method="spearman")
summ<-cbind(a, deparse(substitute(dataset)), target, "rain", nsites, sc.agr$Estimate$CCC[1], sc.agr$Estimate$Precision[1], sc.agr$Estimate$Accuracy[1], W$Concordance_analysis[1], Sp[2])
})
return(summ)
}
multisite.concord.hydro<-function(a, dataset, sites, target, scalar, deltalimit, family) #a is variable in quotes, target is site name in quotes
{
b<-filter(dataset, site%in%sites)%>%filter(site%nin%target)
c<-filter(dataset, site%in%target)
nsites<-nrow(distinct(select(b, site)))
predmatrix<-cbind(c["maxvol"], c["cv.depth"],c["prop.driedout.days"],c["prop.overflow.days"],c["mean.depth"], c["long_dry"], c["last_wet"])
predmatrix[,a]<-c[,a]
nr<-nrow(c)
predmatrix$site<-rep(b[,"site"][1], nr)
summ<-cbind(a, deparse(substitute(dataset)), target, "hydro", nsites ,"NA", "NA", "NA", "NA", "NA", "NA")
try({
if (family=="nb")
{
correction<-mean(log(c[,a]*scalar+0.001))-mean(log(b[1:nr,a]*scalar+0.001))
obs<-log(round(c[,a]*scalar)+0.001)
nbset<-aic.hydro.nb.add(round(b[,a]*scalar), b) #set of nb models
newtheta<-as.numeric(as.vector(levels(nbset[1]$init.theta))[as.numeric(nbset[1]$init.theta)])
allsc<-aic.hydro.add(round(b[,a]*scalar),negative.binomial(theta=newtheta), b) #rerun AIC with fixed theta
}
else
{
correction<-mean(c[,a])-mean(b[1:nr,a])
obs<-c[,a]
allsc<-aic.hydro.add(b[,a],gaussian, b)
}
sc.ests<-get.models(allsc, subset= delta == 0)
preds<- as.vector(sapply(sc.ests, predict, newdata = predmatrix))+correction
try({
sc.ests<-model.avg(allsc, subset= delta < deltalimit, revised.var = TRUE, fit = TRUE)#now get average parameter estimates from plausible set
preds<-as.vector(predict(sc.ests,predmatrix, full = TRUE))+correction
})
predobs<-as.matrix(cbind(preds, obs))
sc.agr$Estimate$CCC[1]<-"NA"
sc.agr$Estimate$Precision[1]<- cor(predobs, use="pairwise.complete.obs", method="pearson")[2]
sc.agr$Estimate$Accuracy[1]<-"NA"
try({sc.agr<-agreement(x=preds, y=obs, error="const",TDI_a = 1, target="random")
})
W<-kendall.global(predobs)
Sp<-cor(predobs, use="pairwise.complete.obs", method="spearman")
summ<-cbind(a, deparse(substitute(dataset)), target, "hydro", nsites, sc.agr$Estimate$CCC[1], sc.agr$Estimate$Precision[1], sc.agr$Estimate$Accuracy[1], W$Concordance_analysis[1], Sp[2])
})
return(summ)
}
multicombo<-function(sites1, response, dataset, scalar, deltalimit, family){
comb1<-combn(sites1, 1)
comb2<-ncol(comb1)
outr<-data.frame(Response=numeric(), Reference= numeric(), Target =numeric(), Model=numeric(), Sites=numeric(), CCC=numeric(),Precision=numeric(),Accuracy=numeric(), Kendall=numeric(), Spearman=numeric())
outh<-data.frame(Response=numeric(), Reference= numeric(), Target =numeric(), Model=numeric(), Sites=numeric(), CCC=numeric(),Precision=numeric(),Accuracy=numeric(), Kendall=numeric(), Spearman=numeric())
for (i in 1:comb2)
{
outr[i,]<- multisite.concord(response, dataset, sites1, comb1[1,i], scalar, deltalimit, family)
outh[i,]<- multisite.concord.hydro(response, dataset, sites1, comb1[1,i], scalar, deltalimit, family)
}
out<-rbind(outr, outh)
return(out)
}
multimachine<-function(response, dataset, sites, scalar, deltalimit, family)
{
for (f in 3:length(sites))
{
k<-nrow(concord.out)
siter<-combn(sites,f)#must be higherthan 3
for (g in 1:ncol(siter))
{
output<- multicombo(siter[,g], response, dataset, scalar, deltalimit, family)
concord.out<-rbind(concord.out, output)
}
}
return(concord.out)}
concord.magic<-function(sites, response, dataset, scalar, deltalimit, family)
{
concord.out<-data.frame(Response=numeric(), Reference= numeric(), Target =numeric(), Model=numeric(), Sites=numeric(), CCC=numeric(),Precision=numeric(),Accuracy=numeric(), Kendall=numeric(), Spearman=numeric())
combin<-combn(sites, 2)
combos<-ncol(combin)
i<-0
for (i in 1:combos)
{
concord.out[i,]<- singlesite.concord(response, dataset, combin[1,i], combin[2,i], scalar, deltalimit, family)
}
j<-nrow(concord.out); i<-0
for (i in 1:combos)
{
concord.out[j+i,]<- singlesite.concord.hydro(response, dataset, combin[1,i], combin[2,i], scalar, deltalimit, family)
}
multiout<-multimachine(response, dataset, sites, scalar, deltalimit, family)
concord.out<-rbind(concord.out, multiout)
concord.out$Reference<-as.factor(concord.out$Reference)
concord.out$Target<-as.factor(concord.out$Target)
concord.out$Model<-as.factor(concord.out$Model)
concord.out$Sites<-as.numeric(concord.out$Sites)
concord.out$CCC<-as.numeric(concord.out$CCC)
concord.out$Kendall<-as.numeric(concord.out$Kendall)
concord.out$Spearman<-as.numeric(concord.out$Spearman)
concord.out$Precision<-as.numeric(concord.out$Precision)
concord.out$Accuracy<-as.numeric(concord.out$Accuracy)
return(concord.out)
}
#
sites<-c("puertorico","argentina", "macae", "frenchguiana")
concord.out1<-data.frame(Response=numeric(), Reference= numeric(), Target =numeric(), Model=numeric(), Sites=numeric(), CCC=numeric(),Precision=numeric(),Accuracy=numeric(), Kendall=numeric(), Spearman=numeric())
concord.out1<-concord.magic(sites, "filter.feeder_bio", fulldata, 100, 2, "nb")
## Warning: glm.fit: algorithm did not converge
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m4 -6.480 1.955 0.6122 -2.996
## m0 -3.409 1.312
## m2 -4.111 1.427 0.5793
## m1 -4.202 1.452 0.2082
## m3 -6.113 1.761 0.3818 0.1200
## m10 -7.478 2.131 0.2138 0.6161 -3.004
## m9 -4.645 1.522 0.2412 0.5839
## m11 -5.905 1.722 0.3945 0.4144 0.1020
## m12 -11.160 2.682 0.9676 0.2736 0.4837 -1.272
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m4
## m0
## m2
## m1
## m3
## m10 -0.3663 0.3816
## m9 -0.3352
## m11 -0.1485
## m12 -0.2375 -1.6880
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 family
## m4 NB(0.5117)
## m0 NB(0.4034)
## m2 NB(0.4117)
## m1 NB(0.411)
## m3 NB(0.413)
## m10 NB(0.5421)
## m9 NB(0.4235)
## m11 0.2834 NB(0.4268)
## m12 0.4983 -2.037 NB(0.6076)
## init.theta df logLik AICc delta weight
## m4 0.512 5 -146.230 305.0 0.00 0.555
## m0 0.403 3 -149.861 306.6 1.69 0.239
## m2 0.412 4 -149.540 308.7 3.72 0.086
## m1 0.411 4 -149.567 308.7 3.78 0.084
## m3 0.413 5 -149.491 311.5 6.52 0.021
## m10 0.542 8 -145.371 313.6 8.64 0.007
## m9 0.423 6 -149.092 313.8 8.88 0.007
## m11 0.427 8 -148.962 320.8 15.82 0.000
## m12 0.608 11 -143.438 323.5 18.58 0.000
## Abbreviations:
## family: NB(0.4034) = 'Negative Binomial(0.4034)',
## NB(0.411) = 'Negative Binomial(0.411)',
## NB(0.4117) = 'Negative Binomial(0.4117)',
## NB(0.413) = 'Negative Binomial(0.413)',
## NB(0.4235) = 'Negative Binomial(0.4235)',
## NB(0.4268) = 'Negative Binomial(0.4268)',
## NB(0.5117) = 'Negative Binomial(0.5117)',
## NB(0.5421) = 'Negative Binomial(0.5421)',
## NB(0.6076) = 'Negative Binomial(0.6076)'
## Models ranked by AICc(x)
## Warning: glm.fit: algorithm did not converge
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m4 -6.479 1.955 0.6122 -2.995
## m0 -3.403 1.310
## m2 -4.105 1.426 0.5783
## m1 -4.195 1.451 0.2083
## m3 -6.097 1.758 0.3813 0.1196
## m10 -7.485 2.132 0.2139 0.6162 -3.004
## m9 -4.641 1.522 0.2412 0.5828
## m11 -5.926 1.726 0.3949 0.4176 0.1025
## m12 -11.120 2.676 0.9660 0.2619 0.4828 -1.258
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m4
## m0
## m2
## m1
## m3
## m10 -0.3669 0.3827
## m9 -0.3339
## m11 -0.1475
## m12 -0.2370 -1.6840
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik
## m4 4 -146.230
## m0 2 -150.334
## m2 3 -149.931
## m1 3 -149.965
## m3 4 -149.870
## m10 7 -145.394
## m9 5 -149.384
## m11 0.2763 7 -149.228
## m12 0.5125 -2.055 10 -143.634
## AICc delta weight
## m4 302.1 0.00 0.685
## m0 305.1 3.05 0.149
## m2 306.8 4.73 0.064
## m1 306.9 4.79 0.062
## m3 309.3 7.28 0.018
## m10 309.9 7.82 0.014
## m9 311.3 9.21 0.007
## m11 317.5 15.49 0.000
## m12 318.8 16.79 0.000
## Models ranked by AICc(x)
## Warning: glm.fit: algorithm did not converge
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m4 -6.480 1.955 0.6122 -2.996
## m0 -3.409 1.312
## m2 -4.111 1.427 0.5793
## m1 -4.202 1.452 0.2082
## m3 -6.113 1.761 0.3818 0.1200
## m10 -7.478 2.131 0.2138 0.6161 -3.004
## m9 -4.645 1.522 0.2412 0.5839
## m11 -5.905 1.722 0.3945 0.4144 0.1020
## m12 -11.160 2.682 0.9676 0.2736 0.4837 -1.272
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m4
## m0
## m2
## m1
## m3
## m10 -0.3663 0.3816
## m9 -0.3352
## m11 -0.1485
## m12 -0.2375 -1.6880
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 family
## m4 NB(0.5117)
## m0 NB(0.4034)
## m2 NB(0.4117)
## m1 NB(0.411)
## m3 NB(0.413)
## m10 NB(0.5421)
## m9 NB(0.4235)
## m11 0.2834 NB(0.4268)
## m12 0.4983 -2.037 NB(0.6076)
## init.theta df logLik AICc delta weight
## m4 0.512 5 -146.230 305.0 0.00 0.555
## m0 0.403 3 -149.861 306.6 1.69 0.239
## m2 0.412 4 -149.540 308.7 3.72 0.086
## m1 0.411 4 -149.567 308.7 3.78 0.084
## m3 0.413 5 -149.491 311.5 6.52 0.021
## m10 0.542 8 -145.371 313.6 8.64 0.007
## m9 0.423 6 -149.092 313.8 8.88 0.007
## m11 0.427 8 -148.962 320.8 15.82 0.000
## m12 0.608 11 -143.438 323.5 18.58 0.000
## Abbreviations:
## family: NB(0.4034) = 'Negative Binomial(0.4034)',
## NB(0.411) = 'Negative Binomial(0.411)',
## NB(0.4117) = 'Negative Binomial(0.4117)',
## NB(0.413) = 'Negative Binomial(0.413)',
## NB(0.4235) = 'Negative Binomial(0.4235)',
## NB(0.4268) = 'Negative Binomial(0.4268)',
## NB(0.5117) = 'Negative Binomial(0.5117)',
## NB(0.5421) = 'Negative Binomial(0.5421)',
## NB(0.6076) = 'Negative Binomial(0.6076)'
## Models ranked by AICc(x)
## Warning: glm.fit: algorithm did not converge
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m4 -6.479 1.955 0.6122 -2.995
## m0 -3.403 1.310
## m2 -4.105 1.426 0.5783
## m1 -4.195 1.451 0.2083
## m3 -6.097 1.758 0.3813 0.1196
## m10 -7.485 2.132 0.2139 0.6162 -3.004
## m9 -4.641 1.522 0.2412 0.5828
## m11 -5.926 1.726 0.3949 0.4176 0.1025
## m12 -11.120 2.676 0.9660 0.2619 0.4828 -1.258
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m4
## m0
## m2
## m1
## m3
## m10 -0.3669 0.3827
## m9 -0.3339
## m11 -0.1475
## m12 -0.2370 -1.6840
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik
## m4 4 -146.230
## m0 2 -150.334
## m2 3 -149.931
## m1 3 -149.965
## m3 4 -149.870
## m10 7 -145.394
## m9 5 -149.384
## m11 0.2763 7 -149.228
## m12 0.5125 -2.055 10 -143.634
## AICc delta weight
## m4 302.1 0.00 0.685
## m0 305.1 3.05 0.149
## m2 306.8 4.73 0.064
## m1 306.9 4.79 0.062
## m3 309.3 7.28 0.018
## m10 309.9 7.82 0.014
## m9 311.3 9.21 0.007
## m11 317.5 15.49 0.000
## m12 318.8 16.79 0.000
## Models ranked by AICc(x)
## Warning: glm.fit: algorithm did not converge
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m4 -6.480 1.955 0.6122 -2.996
## m0 -3.409 1.312
## m2 -4.111 1.427 0.5793
## m1 -4.202 1.452 0.2082
## m3 -6.113 1.761 0.3818 0.1200
## m10 -7.478 2.131 0.2138 0.6161 -3.004
## m9 -4.645 1.522 0.2412 0.5839
## m11 -5.905 1.722 0.3945 0.4144 0.1020
## m12 -11.160 2.682 0.9676 0.2736 0.4837 -1.272
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m4
## m0
## m2
## m1
## m3
## m10 -0.3663 0.3816
## m9 -0.3352
## m11 -0.1485
## m12 -0.2375 -1.6880
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 family
## m4 NB(0.5117)
## m0 NB(0.4034)
## m2 NB(0.4117)
## m1 NB(0.411)
## m3 NB(0.413)
## m10 NB(0.5421)
## m9 NB(0.4235)
## m11 0.2834 NB(0.4268)
## m12 0.4983 -2.037 NB(0.6076)
## init.theta df logLik AICc delta weight
## m4 0.512 5 -146.230 305.0 0.00 0.555
## m0 0.403 3 -149.861 306.6 1.69 0.239
## m2 0.412 4 -149.540 308.7 3.72 0.086
## m1 0.411 4 -149.567 308.7 3.78 0.084
## m3 0.413 5 -149.491 311.5 6.52 0.021
## m10 0.542 8 -145.371 313.6 8.64 0.007
## m9 0.423 6 -149.092 313.8 8.88 0.007
## m11 0.427 8 -148.962 320.8 15.82 0.000
## m12 0.608 11 -143.438 323.5 18.58 0.000
## Abbreviations:
## family: NB(0.4034) = 'Negative Binomial(0.4034)',
## NB(0.411) = 'Negative Binomial(0.411)',
## NB(0.4117) = 'Negative Binomial(0.4117)',
## NB(0.413) = 'Negative Binomial(0.413)',
## NB(0.4235) = 'Negative Binomial(0.4235)',
## NB(0.4268) = 'Negative Binomial(0.4268)',
## NB(0.5117) = 'Negative Binomial(0.5117)',
## NB(0.5421) = 'Negative Binomial(0.5421)',
## NB(0.6076) = 'Negative Binomial(0.6076)'
## Models ranked by AICc(x)
## Warning: glm.fit: algorithm did not converge
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m4 -6.479 1.955 0.6122 -2.995
## m0 -3.403 1.310
## m2 -4.105 1.426 0.5783
## m1 -4.195 1.451 0.2083
## m3 -6.097 1.758 0.3813 0.1196
## m10 -7.485 2.132 0.2139 0.6162 -3.004
## m9 -4.641 1.522 0.2412 0.5828
## m11 -5.926 1.726 0.3949 0.4176 0.1025
## m12 -11.120 2.676 0.9660 0.2619 0.4828 -1.258
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m4
## m0
## m2
## m1
## m3
## m10 -0.3669 0.3827
## m9 -0.3339
## m11 -0.1475
## m12 -0.2370 -1.6840
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik
## m4 4 -146.230
## m0 2 -150.334
## m2 3 -149.931
## m1 3 -149.965
## m3 4 -149.870
## m10 7 -145.394
## m9 5 -149.384
## m11 0.2763 7 -149.228
## m12 0.5125 -2.055 10 -143.634
## AICc delta weight
## m4 302.1 0.00 0.685
## m0 305.1 3.05 0.149
## m2 306.8 4.73 0.064
## m1 306.9 4.79 0.062
## m3 309.3 7.28 0.018
## m10 309.9 7.82 0.014
## m9 311.3 9.21 0.007
## m11 317.5 15.49 0.000
## m12 318.8 16.79 0.000
## Models ranked by AICc(x)
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in glm.nb(y ~ log(maxvol) + log(mu.scalar), data = dataset):
## alternation limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in glm.nb(y ~ log(maxvol) + log(mu.scalar) + I(log(mu.scalar)^2), :
## alternation limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in glm.nb(y ~ log(maxvol) + log(mu.scalar) * log(k.scalar), data =
## dataset): alternation limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in glm.nb(y ~ log(maxvol) + log(mu.scalar) * (log(k.scalar) +
## I(log(k.scalar)^2)), : alternation limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in glm.nb(y ~ log(maxvol) + log(k.scalar) * (log(mu.scalar) +
## I(log(mu.scalar)^2)), : alternation limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m12 -13.850 2.8630 1.9610 1.7590 -0.6523 -0.05067
## m11 -11.840 2.5120 0.9250 1.7730 -0.3521
## m10 -12.680 2.6250 1.5590 1.2550 -0.23330
## m9 -10.890 2.3120 0.9722 1.2410
## m3 -5.238 1.4390 0.5988 -0.5130
## m1 -6.602 1.6170 0.6857
## m4 -3.568 1.1000 1.0350 -0.53090
## m2 -3.506 1.0630 0.9483
## m0 -2.418 0.8989
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m12 -0.4408 -3.048
## m11 -0.7487
## m10 -0.1794 -1.906
## m9 -0.5574
## m3
## m1
## m4
## m2
## m0
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 family
## m12 -0.7346 0.2422 NB(969478.1)
## m11 -0.7636 NB(680159)
## m10 NB(291138.5)
## m9 NB(423948.2)
## m3 NB(203505)
## m1 NB(114439.7)
## m4 NB(309596.7)
## m2 NB(264000.4)
## m0 NB(177812.9)
## init.theta df logLik AICc delta weight
## m12 969000 11 -199.121 434.9 0.00 1
## m11 680000 8 -241.942 506.7 71.83 0
## m10 291000 8 -266.905 556.7 121.76 0
## m9 424000 6 -294.678 605.0 170.10 0
## m3 204000 5 -358.968 730.4 295.53 0
## m1 114000 4 -384.912 779.4 344.52 0
## m4 310000 5 -403.339 819.2 384.27 0
## m2 264000 4 -406.279 822.2 387.25 0
## m0 178000 3 -470.932 948.8 513.88 0
## Abbreviations:
## family: NB(114439.7) = 'Negative Binomial(114439.7)',
## NB(177812.9) = 'Negative Binomial(177812.9)',
## NB(203505) = 'Negative Binomial(203505)',
## NB(264000.4) = 'Negative Binomial(264000.4)',
## NB(291138.5) = 'Negative Binomial(291138.5)',
## NB(309596.7) = 'Negative Binomial(309596.7)',
## NB(423948.2) = 'Negative Binomial(423948.2)',
## NB(680159) = 'Negative Binomial(680159)',
## NB(969478.1) = 'Negative Binomial(969478.1)'
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m12 -13.850 2.8630 1.9610 1.7590 -0.6523 -0.05067
## m11 -11.840 2.5120 0.9250 1.7730 -0.3521
## m10 -12.680 2.6250 1.5590 1.2550 -0.23330
## m9 -10.890 2.3120 0.9722 1.2410
## m3 -5.238 1.4390 0.5987 -0.5129
## m1 -6.601 1.6170 0.6857
## m4 -3.568 1.1000 1.0350 -0.53090
## m2 -3.506 1.0630 0.9483
## m0 -2.418 0.8989
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m12 -0.4408 -3.048
## m11 -0.7487
## m10 -0.1794 -1.906
## m9 -0.5575
## m3
## m1
## m4
## m2
## m0
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik
## m12 -0.7346 0.2422 10 -199.121
## m11 -0.7636 7 -241.944
## m10 7 -266.915
## m9 5 -294.684
## m3 4 -358.991
## m1 3 -384.957
## m4 4 -403.352
## m2 3 -406.295
## m0 2 -470.963
## AICc delta weight
## m12 429.8 0.00 1
## m11 503.0 73.16 0
## m10 552.9 123.10 0
## m9 601.9 172.05 0
## m3 727.6 297.76 0
## m1 776.8 347.02 0
## m4 816.3 386.48 0
## m2 819.5 389.69 0
## m0 946.4 516.55 0
## Models ranked by AICc(x)
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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## Warning in sqrt(1/i): NaNs produced
## Warning in glm.nb(y ~ log(maxvol) + log(mu.scalar), data = dataset):
## alternation limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
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## Warning in glm.nb(y ~ log(maxvol) + log(mu.scalar) + I(log(mu.scalar)^2), :
## alternation limit reached
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## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
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## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in glm.nb(y ~ log(maxvol) + log(mu.scalar) * log(k.scalar), data =
## dataset): alternation limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
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## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
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## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
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## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
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## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
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## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
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## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in glm.nb(y ~ log(maxvol) + log(mu.scalar) * (log(k.scalar) +
## I(log(k.scalar)^2)), : alternation limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in glm.nb(y ~ log(maxvol) + log(k.scalar) * (log(mu.scalar) +
## I(log(mu.scalar)^2)), : alternation limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m12 -13.850 2.8630 1.9610 1.7590 -0.6523 -0.05067
## m11 -11.840 2.5120 0.9250 1.7730 -0.3521
## m10 -12.680 2.6250 1.5590 1.2550 -0.23330
## m9 -10.890 2.3120 0.9722 1.2410
## m3 -5.238 1.4390 0.5988 -0.5130
## m1 -6.602 1.6170 0.6857
## m4 -3.568 1.1000 1.0350 -0.53090
## m2 -3.506 1.0630 0.9483
## m0 -2.418 0.8989
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m12 -0.4408 -3.048
## m11 -0.7487
## m10 -0.1794 -1.906
## m9 -0.5574
## m3
## m1
## m4
## m2
## m0
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 family
## m12 -0.7346 0.2422 NB(969478.1)
## m11 -0.7636 NB(680159)
## m10 NB(291138.5)
## m9 NB(423948.2)
## m3 NB(203505)
## m1 NB(114439.7)
## m4 NB(309596.7)
## m2 NB(264000.4)
## m0 NB(177812.9)
## init.theta df logLik AICc delta weight
## m12 969000 11 -199.121 434.9 0.00 1
## m11 680000 8 -241.942 506.7 71.83 0
## m10 291000 8 -266.905 556.7 121.76 0
## m9 424000 6 -294.678 605.0 170.10 0
## m3 204000 5 -358.968 730.4 295.53 0
## m1 114000 4 -384.912 779.4 344.52 0
## m4 310000 5 -403.339 819.2 384.27 0
## m2 264000 4 -406.279 822.2 387.25 0
## m0 178000 3 -470.932 948.8 513.88 0
## Abbreviations:
## family: NB(114439.7) = 'Negative Binomial(114439.7)',
## NB(177812.9) = 'Negative Binomial(177812.9)',
## NB(203505) = 'Negative Binomial(203505)',
## NB(264000.4) = 'Negative Binomial(264000.4)',
## NB(291138.5) = 'Negative Binomial(291138.5)',
## NB(309596.7) = 'Negative Binomial(309596.7)',
## NB(423948.2) = 'Negative Binomial(423948.2)',
## NB(680159) = 'Negative Binomial(680159)',
## NB(969478.1) = 'Negative Binomial(969478.1)'
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m12 -13.850 2.8630 1.9610 1.7590 -0.6523 -0.05067
## m11 -11.840 2.5120 0.9250 1.7730 -0.3521
## m10 -12.680 2.6250 1.5590 1.2550 -0.23330
## m9 -10.890 2.3120 0.9722 1.2410
## m3 -5.238 1.4390 0.5987 -0.5129
## m1 -6.601 1.6170 0.6857
## m4 -3.568 1.1000 1.0350 -0.53090
## m2 -3.506 1.0630 0.9483
## m0 -2.418 0.8989
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m12 -0.4408 -3.048
## m11 -0.7487
## m10 -0.1794 -1.906
## m9 -0.5575
## m3
## m1
## m4
## m2
## m0
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik
## m12 -0.7346 0.2422 10 -199.121
## m11 -0.7636 7 -241.944
## m10 7 -266.915
## m9 5 -294.684
## m3 4 -358.991
## m1 3 -384.957
## m4 4 -403.352
## m2 3 -406.295
## m0 2 -470.963
## AICc delta weight
## m12 429.8 0.00 1
## m11 503.0 73.16 0
## m10 552.9 123.10 0
## m9 601.9 172.05 0
## m3 727.6 297.76 0
## m1 776.8 347.02 0
## m4 816.3 386.48 0
## m2 819.5 389.69 0
## m0 946.4 516.55 0
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 14.39 -1.331
## m1 14.84 -1.392 0.09134
## m2 13.16 -1.152 -0.09349
## m3 15.37 -1.465 0.05680 -0.03351
## m4 14.16 -1.284 -0.08670 -0.2926
## m9 13.66 -1.223 0.08965 -0.09171
## m10 14.77 -1.368 0.16240 -0.08578 -0.3298
## m11 16.10 -1.569 0.04264 0.02953 -0.04956
## m12 13.01 -1.142 0.33540 0.01992 0.18380 0.3873
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m1
## m2
## m3
## m4
## m9 -0.01137
## m10 -0.01740 -0.2642
## m11 -0.16760
## m12 -0.18660 -1.0460
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 family
## m0 NB(3.6086)
## m1 NB(3.7188)
## m2 NB(3.6381)
## m3 NB(3.7346)
## m4 NB(3.6925)
## m9 NB(3.7473)
## m10 NB(3.8519)
## m11 -0.1425 NB(3.8415)
## m12 -0.1574 -0.7668 NB(4.5122)
## init.theta df logLik AICc delta weight
## m0 3.61 3 -176.904 360.7 0.00 0.516
## m1 3.72 4 -176.430 362.5 1.73 0.217
## m2 3.64 4 -176.776 363.2 2.42 0.154
## m3 3.73 5 -176.362 365.2 4.49 0.055
## m4 3.69 5 -176.543 365.6 4.86 0.046
## m9 3.75 6 -176.310 368.3 7.54 0.012
## m10 3.85 8 -175.879 374.6 13.88 0.000
## m11 3.84 8 -175.905 374.7 13.94 0.000
## m12 4.51 11 -173.381 383.4 22.70 0.000
## Abbreviations:
## family: NB(3.6086) = 'Negative Binomial(3.6086)',
## NB(3.6381) = 'Negative Binomial(3.6381)',
## NB(3.6925) = 'Negative Binomial(3.6925)',
## NB(3.7188) = 'Negative Binomial(3.7188)',
## NB(3.7346) = 'Negative Binomial(3.7346)',
## NB(3.7473) = 'Negative Binomial(3.7473)',
## NB(3.8415) = 'Negative Binomial(3.8415)',
## NB(3.8519) = 'Negative Binomial(3.8519)',
## NB(4.5122) = 'Negative Binomial(4.5122)'
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 14.39 -1.331
## m1 14.84 -1.393 0.09133
## m2 13.16 -1.152 -0.09349
## m3 15.37 -1.465 0.05678 -0.03352
## m4 14.16 -1.284 -0.08668 -0.2925
## m9 13.67 -1.223 0.08964 -0.09170
## m10 14.77 -1.369 0.16230 -0.08576 -0.3297
## m11 16.11 -1.570 0.04258 0.02965 -0.04961
## m12 13.05 -1.148 0.33510 0.02062 0.18360 0.3870
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m1
## m2
## m3
## m4
## m9 -0.01139
## m10 -0.01745 -0.2642
## m11 -0.16780
## m12 -0.18740 -1.0460
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik
## m0 2 -176.904
## m1 3 -176.437
## m2 3 -176.777
## m3 4 -176.371
## m4 4 -176.547
## m9 5 -176.321
## m10 7 -175.911
## m11 -0.1427 7 -175.934
## m12 -0.1580 -0.7668 10 -173.736
## AICc delta weight
## m0 358.3 0.00 0.484
## m1 359.8 1.54 0.224
## m2 360.5 2.22 0.159
## m3 362.3 4.09 0.063
## m4 362.7 4.44 0.053
## m9 365.1 6.89 0.015
## m10 370.9 12.66 0.001
## m11 371.0 12.71 0.001
## m12 379.1 20.80 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) cv.dpt prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m3 -0.30690 0.9191 -7.808
## m1 0.61090 0.9000 -0.03703
## m5 3.43700 0.2513 -0.1801
## m4 0.06434 0.3631 0.05072
## m6 -2.73800 1.2830
## m0 -3.40900 1.3120
## m2 -1.54500 0.8175 5.047
## lst_wet family init.theta df logLik AICc delta weight
## m3 NB(0.6465) 0.647 4 -142.288 294.2 0.00 0.503
## m1 NB(0.635) 0.635 4 -142.628 294.9 0.68 0.358
## m5 NB(0.5667) 0.567 4 -143.876 297.4 3.18 0.103
## m4 NB(0.5557) 0.556 4 -145.036 299.7 5.50 0.032
## m6 -0.04529 NB(0.4514) 0.451 4 -148.090 305.8 11.60 0.002
## m0 NB(0.4034) 0.403 3 -149.861 306.6 12.47 0.001
## m2 NB(0.4363) 0.436 4 -148.629 306.9 12.68 0.001
## Abbreviations:
## family: NB(0.4034) = 'Negative Binomial(0.4034)',
## NB(0.4363) = 'Negative Binomial(0.4363)',
## NB(0.4514) = 'Negative Binomial(0.4514)',
## NB(0.5557) = 'Negative Binomial(0.5557)',
## NB(0.5667) = 'Negative Binomial(0.5667)',
## NB(0.635) = 'Negative Binomial(0.635)',
## NB(0.6465) = 'Negative Binomial(0.6465)'
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m3 -0.30620 0.9189 -7.807
## m1 0.61790 -0.03699 0.8984
## m5 3.40100 0.2563 -0.1784
## m4 0.08811 0.3597 0.05063
## m6 -2.69500 1.2750
## m2 -1.53300 0.8155 5.042
## lst_wet df logLik AICc delta weight
## m3 3 -142.288 291.5 0.00 0.514
## m1 3 -142.630 292.2 0.68 0.365
## m5 3 -144.009 294.9 3.44 0.092
## m4 3 -145.202 297.3 5.83 0.028
## m6 -0.04512 3 -149.152 305.2 13.73 0.001
## m2 3 -149.924 306.8 15.27 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) cv.dpt prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m3 -0.30690 0.9191 -7.808
## m1 0.61090 0.9000 -0.03703
## m5 3.43700 0.2513 -0.1801
## m4 0.06434 0.3631 0.05072
## m6 -2.73800 1.2830
## m0 -3.40900 1.3120
## m2 -1.54500 0.8175 5.047
## lst_wet family init.theta df logLik AICc delta weight
## m3 NB(0.6465) 0.647 4 -142.288 294.2 0.00 0.503
## m1 NB(0.635) 0.635 4 -142.628 294.9 0.68 0.358
## m5 NB(0.5667) 0.567 4 -143.876 297.4 3.18 0.103
## m4 NB(0.5557) 0.556 4 -145.036 299.7 5.50 0.032
## m6 -0.04529 NB(0.4514) 0.451 4 -148.090 305.8 11.60 0.002
## m0 NB(0.4034) 0.403 3 -149.861 306.6 12.47 0.001
## m2 NB(0.4363) 0.436 4 -148.629 306.9 12.68 0.001
## Abbreviations:
## family: NB(0.4034) = 'Negative Binomial(0.4034)',
## NB(0.4363) = 'Negative Binomial(0.4363)',
## NB(0.4514) = 'Negative Binomial(0.4514)',
## NB(0.5557) = 'Negative Binomial(0.5557)',
## NB(0.5667) = 'Negative Binomial(0.5667)',
## NB(0.635) = 'Negative Binomial(0.635)',
## NB(0.6465) = 'Negative Binomial(0.6465)'
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m3 -0.30620 0.9189 -7.807
## m1 0.61790 -0.03699 0.8984
## m5 3.40100 0.2563 -0.1784
## m4 0.08811 0.3597 0.05063
## m6 -2.69500 1.2750
## m2 -1.53300 0.8155 5.042
## lst_wet df logLik AICc delta weight
## m3 3 -142.288 291.5 0.00 0.514
## m1 3 -142.630 292.2 0.68 0.365
## m5 3 -144.009 294.9 3.44 0.092
## m4 3 -145.202 297.3 5.83 0.028
## m6 -0.04512 3 -149.152 305.2 13.73 0.001
## m2 3 -149.924 306.8 15.27 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) cv.dpt prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m3 -0.30690 0.9191 -7.808
## m1 0.61090 0.9000 -0.03703
## m5 3.43700 0.2513 -0.1801
## m4 0.06434 0.3631 0.05072
## m6 -2.73800 1.2830
## m0 -3.40900 1.3120
## m2 -1.54500 0.8175 5.047
## lst_wet family init.theta df logLik AICc delta weight
## m3 NB(0.6465) 0.647 4 -142.288 294.2 0.00 0.503
## m1 NB(0.635) 0.635 4 -142.628 294.9 0.68 0.358
## m5 NB(0.5667) 0.567 4 -143.876 297.4 3.18 0.103
## m4 NB(0.5557) 0.556 4 -145.036 299.7 5.50 0.032
## m6 -0.04529 NB(0.4514) 0.451 4 -148.090 305.8 11.60 0.002
## m0 NB(0.4034) 0.403 3 -149.861 306.6 12.47 0.001
## m2 NB(0.4363) 0.436 4 -148.629 306.9 12.68 0.001
## Abbreviations:
## family: NB(0.4034) = 'Negative Binomial(0.4034)',
## NB(0.4363) = 'Negative Binomial(0.4363)',
## NB(0.4514) = 'Negative Binomial(0.4514)',
## NB(0.5557) = 'Negative Binomial(0.5557)',
## NB(0.5667) = 'Negative Binomial(0.5667)',
## NB(0.635) = 'Negative Binomial(0.635)',
## NB(0.6465) = 'Negative Binomial(0.6465)'
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m3 -0.30620 0.9189 -7.807
## m1 0.61790 -0.03699 0.8984
## m5 3.40100 0.2563 -0.1784
## m4 0.08811 0.3597 0.05063
## m6 -2.69500 1.2750
## m2 -1.53300 0.8155 5.042
## lst_wet df logLik AICc delta weight
## m3 3 -142.288 291.5 0.00 0.514
## m1 3 -142.630 292.2 0.68 0.365
## m5 3 -144.009 294.9 3.44 0.092
## m4 3 -145.202 297.3 5.83 0.028
## m6 -0.04512 3 -149.152 305.2 13.73 0.001
## m2 3 -149.924 306.8 15.27 0.000
## Models ranked by AICc(x)
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in glm.nb(y ~ log(maxvol) + cv.depth, data = dataset): alternation
## limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in glm.nb(y ~ log(maxvol) + mean.depth, data = dataset):
## alternation limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in glm.nb(y ~ log(maxvol) + last_wet, data = dataset): alternation
## limit reached
## Model selection table
## (Int) log(mxv) cv.dpt prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m3 3.1430 0.2456 -9.643
## m5 1.8010 0.4425 -0.3452
## m1 0.8680 0.5762 -0.02076
## m4 -0.4797 0.3471 0.03235
## m6 -6.3420 1.7430
## m2 -3.6220 1.0270 3.731
## m0 -2.4180 0.8989
## lst_wet family init.theta df logLik AICc delta weight
## m3 NB(0.2452) 0.245 4 -86.809 183.2 0.00 0.85
## m5 NB(0.2103) 0.21 4 -88.547 186.7 3.48 0.15
## m1 NB(193937.3) 194000 4 -344.664 698.9 515.71 0.00
## m4 NB(197097.4) 197000 4 -388.957 787.5 604.30 0.00
## m6 -0.04489 NB(125101.8) 125000 4 -392.766 795.1 611.91 0.00
## m2 NB(174595.5) 175000 4 -449.478 908.6 725.34 0.00
## m0 NB(177812.9) 178000 3 -470.932 948.8 765.57 0.00
## Abbreviations:
## family: NB(0.2103) = 'Negative Binomial(0.2103)',
## NB(0.2452) = 'Negative Binomial(0.2452)',
## NB(125101.8) = 'Negative Binomial(125101.8)',
## NB(174595.5) = 'Negative Binomial(174595.5)',
## NB(177812.9) = 'Negative Binomial(177812.9)',
## NB(193937.3) = 'Negative Binomial(193937.3)',
## NB(197097.4) = 'Negative Binomial(197097.4)'
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m1 1.9510 -0.05016 0.72080
## m3 3.1420 0.24570 -9.643
## m5 1.6700 0.46040 -0.3396
## m4 0.3576 -0.03315 0.06827
## m6 -12.1700 2.81400
## m2 -6.1920 1.44100 5.096
## lst_wet df logLik AICc delta weight
## m1 3 -86.061 179.0 0.00 0.629
## m3 3 -86.809 180.5 1.50 0.298
## m5 3 -88.655 184.2 5.19 0.047
## m4 3 -89.818 186.6 7.51 0.015
## m6 -0.06175 3 -90.318 187.6 8.51 0.009
## m2 3 -91.658 190.2 11.19 0.002
## Models ranked by AICc(x)
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in glm.nb(y ~ log(maxvol) + cv.depth, data = dataset): alternation
## limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in glm.nb(y ~ log(maxvol) + mean.depth, data = dataset):
## alternation limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in glm.nb(y ~ log(maxvol) + last_wet, data = dataset): alternation
## limit reached
## Model selection table
## (Int) log(mxv) cv.dpt prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m3 3.1430 0.2456 -9.643
## m5 1.8010 0.4425 -0.3452
## m1 0.8680 0.5762 -0.02076
## m4 -0.4797 0.3471 0.03235
## m6 -6.3420 1.7430
## m2 -3.6220 1.0270 3.731
## m0 -2.4180 0.8989
## lst_wet family init.theta df logLik AICc delta weight
## m3 NB(0.2452) 0.245 4 -86.809 183.2 0.00 0.85
## m5 NB(0.2103) 0.21 4 -88.547 186.7 3.48 0.15
## m1 NB(193937.3) 194000 4 -344.664 698.9 515.71 0.00
## m4 NB(197097.4) 197000 4 -388.957 787.5 604.30 0.00
## m6 -0.04489 NB(125101.8) 125000 4 -392.766 795.1 611.91 0.00
## m2 NB(174595.5) 175000 4 -449.478 908.6 725.34 0.00
## m0 NB(177812.9) 178000 3 -470.932 948.8 765.57 0.00
## Abbreviations:
## family: NB(0.2103) = 'Negative Binomial(0.2103)',
## NB(0.2452) = 'Negative Binomial(0.2452)',
## NB(125101.8) = 'Negative Binomial(125101.8)',
## NB(174595.5) = 'Negative Binomial(174595.5)',
## NB(177812.9) = 'Negative Binomial(177812.9)',
## NB(193937.3) = 'Negative Binomial(193937.3)',
## NB(197097.4) = 'Negative Binomial(197097.4)'
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m1 1.9510 -0.05016 0.72080
## m3 3.1420 0.24570 -9.643
## m5 1.6700 0.46040 -0.3396
## m4 0.3576 -0.03315 0.06827
## m6 -12.1700 2.81400
## m2 -6.1920 1.44100 5.096
## lst_wet df logLik AICc delta weight
## m1 3 -86.061 179.0 0.00 0.629
## m3 3 -86.809 180.5 1.50 0.298
## m5 3 -88.655 184.2 5.19 0.047
## m4 3 -89.818 186.6 7.51 0.015
## m6 -0.06175 3 -90.318 187.6 8.51 0.009
## m2 3 -91.658 190.2 11.19 0.002
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) cv.dpt prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m0 14.39 -1.331
## m4 13.12 -1.210 0.006571
## m6 14.10 -1.274
## m1 13.92 -1.255 -0.001172
## m2 14.44 -1.346 0.9702
## m5 14.32 -1.319 -0.002674
## m3 14.43 -1.337 0.04553
## lst_wet family init.theta df logLik AICc delta weight
## m0 NB(3.6086) 3.61 3 -176.904 360.7 0.00 0.304
## m4 NB(3.8915) 3.89 4 -175.721 361.0 0.31 0.260
## m6 -0.003354 NB(3.6628) 3.66 4 -176.667 362.9 2.20 0.101
## m1 NB(3.6305) 3.63 4 -176.809 363.2 2.49 0.088
## m2 NB(3.6277) 3.63 4 -176.820 363.2 2.51 0.087
## m5 NB(3.6114) 3.61 4 -176.892 363.4 2.65 0.081
## m3 NB(3.6091) 3.61 4 -176.902 363.4 2.67 0.080
## Abbreviations:
## family: NB(3.6086) = 'Negative Binomial(3.6086)',
## NB(3.6091) = 'Negative Binomial(3.6091)',
## NB(3.6114) = 'Negative Binomial(3.6114)',
## NB(3.6277) = 'Negative Binomial(3.6277)',
## NB(3.6305) = 'Negative Binomial(3.6305)',
## NB(3.6628) = 'Negative Binomial(3.6628)',
## NB(3.8915) = 'Negative Binomial(3.8915)'
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m4 13.13 -1.210 0.006568
## m6 14.10 -1.274
## m1 13.92 -0.001171 -1.255
## m2 14.44 -1.346 0.9702
## m5 14.32 -1.319 -0.002674
## m3 14.43 -1.337 0.04552
## lst_wet df logLik AICc delta weight
## m4 3 -175.763 358.4 0.00 0.364
## m6 -0.003354 3 -176.669 360.3 1.81 0.147
## m1 3 -176.809 360.5 2.09 0.128
## m2 3 -176.820 360.6 2.11 0.126
## m5 3 -176.892 360.7 2.26 0.118
## m3 3 -176.902 360.7 2.28 0.117
## Models ranked by AICc(x)
## Error in nrow(concord.out): object 'concord.out' not found
sites<-c("puertorico","argentina", "macae", "frenchguiana", "costarica", "colombia")
concord.out2<-concord.magic(sites, "scraper_bio", nocadata, 100, 2, "nb")#works fine now save 2 models
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 3.112000 0.5057
## m1 1.570000 0.7742 0.1568
## m4 -0.001833 1.1110 -0.013620 -1.4760
## m2 3.239000 0.4839 -0.063110
## m3 0.390600 0.9621 0.2772 0.09098
## m9 1.735000 0.7453 0.1612 -0.061460
## m10 -3.051000 1.6520 0.3023 0.009347 -1.7350
## m11 1.036000 0.8549 0.2450 -0.125000 0.06460
## m12 -8.351000 2.4940 0.9790 0.045340 0.47220 -0.9645
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m1
## m4
## m2
## m3
## m9 -0.18610
## m10 -0.16790 -0.2703
## m11 -0.07851
## m12 -0.16010 -1.6990
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 family
## m0 NB(1.2186)
## m1 NB(1.2479)
## m4 NB(1.346)
## m2 NB(1.2198)
## m3 NB(1.2577)
## m9 NB(1.2598)
## m10 NB(1.4371)
## m11 0.08792 NB(1.2696)
## m12 0.01427 -1.057 NB(1.5443)
## init.theta df logLik AICc delta weight
## m0 1.22 3 -211.709 430.3 0.00 0.457
## m1 1.25 4 -211.273 432.1 1.81 0.185
## m4 1.35 5 -209.903 432.3 1.96 0.171
## m2 1.22 4 -211.691 433.0 2.64 0.122
## m3 1.26 5 -211.131 434.8 4.42 0.050
## m9 1.26 6 -211.101 437.9 7.51 0.011
## m10 1.44 8 -208.729 440.3 9.97 0.003
## m11 1.27 8 -210.961 444.8 14.44 0.000
## m12 1.54 11 -207.455 451.6 21.24 0.000
## Abbreviations:
## family: NB(1.2186) = 'Negative Binomial(1.2186)',
## NB(1.2198) = 'Negative Binomial(1.2198)',
## NB(1.2479) = 'Negative Binomial(1.2479)',
## NB(1.2577) = 'Negative Binomial(1.2577)',
## NB(1.2598) = 'Negative Binomial(1.2598)',
## NB(1.2696) = 'Negative Binomial(1.2696)',
## NB(1.346) = 'Negative Binomial(1.346)',
## NB(1.4371) = 'Negative Binomial(1.4371)',
## NB(1.5443) = 'Negative Binomial(1.5443)'
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 3.112000 0.5057
## m1 1.570000 0.7742 0.1568
## m4 -0.004181 1.1120 -0.013580 -1.4760
## m2 3.239000 0.4839 -0.063130
## m3 0.389700 0.9623 0.2772 0.09101
## m9 1.735000 0.7453 0.1612 -0.061480
## m10 -3.059000 1.6530 0.3025 0.009465 -1.7360
## m11 1.035000 0.8551 0.2451 -0.125000 0.06465
## m12 -8.376000 2.4980 0.9805 0.045930 0.47280 -0.9655
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m1
## m4
## m2
## m3
## m9 -0.18620
## m10 -0.16780 -0.2704
## m11 -0.07852
## m12 -0.16040 -1.7010
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik
## m0 2 -211.709
## m1 3 -211.278
## m4 4 -209.987
## m2 3 -211.691
## m3 4 -211.140
## m9 5 -211.110
## m10 7 -208.956
## m11 0.08787 7 -210.975
## m12 0.01388 -1.059 10 -207.910
## AICc delta weight
## m0 427.9 0.00 0.427
## m1 429.5 1.62 0.190
## m4 429.6 1.71 0.181
## m2 430.3 2.44 0.126
## m3 431.9 4.02 0.057
## m9 434.7 6.86 0.014
## m10 437.0 9.14 0.004
## m11 441.0 13.18 0.001
## m12 447.4 19.54 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 3.112000 0.5057
## m1 1.570000 0.7742 0.1568
## m4 -0.001833 1.1110 -0.013620 -1.4760
## m2 3.239000 0.4839 -0.063110
## m3 0.390600 0.9621 0.2772 0.09098
## m9 1.735000 0.7453 0.1612 -0.061460
## m10 -3.051000 1.6520 0.3023 0.009347 -1.7350
## m11 1.036000 0.8549 0.2450 -0.125000 0.06460
## m12 -8.351000 2.4940 0.9790 0.045340 0.47220 -0.9645
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m1
## m4
## m2
## m3
## m9 -0.18610
## m10 -0.16790 -0.2703
## m11 -0.07851
## m12 -0.16010 -1.6990
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 family
## m0 NB(1.2186)
## m1 NB(1.2479)
## m4 NB(1.346)
## m2 NB(1.2198)
## m3 NB(1.2577)
## m9 NB(1.2598)
## m10 NB(1.4371)
## m11 0.08792 NB(1.2696)
## m12 0.01427 -1.057 NB(1.5443)
## init.theta df logLik AICc delta weight
## m0 1.22 3 -211.709 430.3 0.00 0.457
## m1 1.25 4 -211.273 432.1 1.81 0.185
## m4 1.35 5 -209.903 432.3 1.96 0.171
## m2 1.22 4 -211.691 433.0 2.64 0.122
## m3 1.26 5 -211.131 434.8 4.42 0.050
## m9 1.26 6 -211.101 437.9 7.51 0.011
## m10 1.44 8 -208.729 440.3 9.97 0.003
## m11 1.27 8 -210.961 444.8 14.44 0.000
## m12 1.54 11 -207.455 451.6 21.24 0.000
## Abbreviations:
## family: NB(1.2186) = 'Negative Binomial(1.2186)',
## NB(1.2198) = 'Negative Binomial(1.2198)',
## NB(1.2479) = 'Negative Binomial(1.2479)',
## NB(1.2577) = 'Negative Binomial(1.2577)',
## NB(1.2598) = 'Negative Binomial(1.2598)',
## NB(1.2696) = 'Negative Binomial(1.2696)',
## NB(1.346) = 'Negative Binomial(1.346)',
## NB(1.4371) = 'Negative Binomial(1.4371)',
## NB(1.5443) = 'Negative Binomial(1.5443)'
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 3.112000 0.5057
## m1 1.570000 0.7742 0.1568
## m4 -0.004181 1.1120 -0.013580 -1.4760
## m2 3.239000 0.4839 -0.063130
## m3 0.389700 0.9623 0.2772 0.09101
## m9 1.735000 0.7453 0.1612 -0.061480
## m10 -3.059000 1.6530 0.3025 0.009465 -1.7360
## m11 1.035000 0.8551 0.2451 -0.125000 0.06465
## m12 -8.376000 2.4980 0.9805 0.045930 0.47280 -0.9655
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m1
## m4
## m2
## m3
## m9 -0.18620
## m10 -0.16780 -0.2704
## m11 -0.07852
## m12 -0.16040 -1.7010
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik
## m0 2 -211.709
## m1 3 -211.278
## m4 4 -209.987
## m2 3 -211.691
## m3 4 -211.140
## m9 5 -211.110
## m10 7 -208.956
## m11 0.08787 7 -210.975
## m12 0.01388 -1.059 10 -207.910
## AICc delta weight
## m0 427.9 0.00 0.427
## m1 429.5 1.62 0.190
## m4 429.6 1.71 0.181
## m2 430.3 2.44 0.126
## m3 431.9 4.02 0.057
## m9 434.7 6.86 0.014
## m10 437.0 9.14 0.004
## m11 441.0 13.18 0.001
## m12 447.4 19.54 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 3.112000 0.5057
## m1 1.570000 0.7742 0.1568
## m4 -0.001833 1.1110 -0.013620 -1.4760
## m2 3.239000 0.4839 -0.063110
## m3 0.390600 0.9621 0.2772 0.09098
## m9 1.735000 0.7453 0.1612 -0.061460
## m10 -3.051000 1.6520 0.3023 0.009347 -1.7350
## m11 1.036000 0.8549 0.2450 -0.125000 0.06460
## m12 -8.351000 2.4940 0.9790 0.045340 0.47220 -0.9645
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m1
## m4
## m2
## m3
## m9 -0.18610
## m10 -0.16790 -0.2703
## m11 -0.07851
## m12 -0.16010 -1.6990
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 family
## m0 NB(1.2186)
## m1 NB(1.2479)
## m4 NB(1.346)
## m2 NB(1.2198)
## m3 NB(1.2577)
## m9 NB(1.2598)
## m10 NB(1.4371)
## m11 0.08792 NB(1.2696)
## m12 0.01427 -1.057 NB(1.5443)
## init.theta df logLik AICc delta weight
## m0 1.22 3 -211.709 430.3 0.00 0.457
## m1 1.25 4 -211.273 432.1 1.81 0.185
## m4 1.35 5 -209.903 432.3 1.96 0.171
## m2 1.22 4 -211.691 433.0 2.64 0.122
## m3 1.26 5 -211.131 434.8 4.42 0.050
## m9 1.26 6 -211.101 437.9 7.51 0.011
## m10 1.44 8 -208.729 440.3 9.97 0.003
## m11 1.27 8 -210.961 444.8 14.44 0.000
## m12 1.54 11 -207.455 451.6 21.24 0.000
## Abbreviations:
## family: NB(1.2186) = 'Negative Binomial(1.2186)',
## NB(1.2198) = 'Negative Binomial(1.2198)',
## NB(1.2479) = 'Negative Binomial(1.2479)',
## NB(1.2577) = 'Negative Binomial(1.2577)',
## NB(1.2598) = 'Negative Binomial(1.2598)',
## NB(1.2696) = 'Negative Binomial(1.2696)',
## NB(1.346) = 'Negative Binomial(1.346)',
## NB(1.4371) = 'Negative Binomial(1.4371)',
## NB(1.5443) = 'Negative Binomial(1.5443)'
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 3.112000 0.5057
## m1 1.570000 0.7742 0.1568
## m4 -0.004181 1.1120 -0.013580 -1.4760
## m2 3.239000 0.4839 -0.063130
## m3 0.389700 0.9623 0.2772 0.09101
## m9 1.735000 0.7453 0.1612 -0.061480
## m10 -3.059000 1.6530 0.3025 0.009465 -1.7360
## m11 1.035000 0.8551 0.2451 -0.125000 0.06465
## m12 -8.376000 2.4980 0.9805 0.045930 0.47280 -0.9655
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m1
## m4
## m2
## m3
## m9 -0.18620
## m10 -0.16780 -0.2704
## m11 -0.07852
## m12 -0.16040 -1.7010
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik
## m0 2 -211.709
## m1 3 -211.278
## m4 4 -209.987
## m2 3 -211.691
## m3 4 -211.140
## m9 5 -211.110
## m10 7 -208.956
## m11 0.08787 7 -210.975
## m12 0.01388 -1.059 10 -207.910
## AICc delta weight
## m0 427.9 0.00 0.427
## m1 429.5 1.62 0.190
## m4 429.6 1.71 0.181
## m2 430.3 2.44 0.126
## m3 431.9 4.02 0.057
## m9 434.7 6.86 0.014
## m10 437.0 9.14 0.004
## m11 441.0 13.18 0.001
## m12 447.4 19.54 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 3.112000 0.5057
## m1 1.570000 0.7742 0.1568
## m4 -0.001833 1.1110 -0.013620 -1.4760
## m2 3.239000 0.4839 -0.063110
## m3 0.390600 0.9621 0.2772 0.09098
## m9 1.735000 0.7453 0.1612 -0.061460
## m10 -3.051000 1.6520 0.3023 0.009347 -1.7350
## m11 1.036000 0.8549 0.2450 -0.125000 0.06460
## m12 -8.351000 2.4940 0.9790 0.045340 0.47220 -0.9645
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m1
## m4
## m2
## m3
## m9 -0.18610
## m10 -0.16790 -0.2703
## m11 -0.07851
## m12 -0.16010 -1.6990
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 family
## m0 NB(1.2186)
## m1 NB(1.2479)
## m4 NB(1.346)
## m2 NB(1.2198)
## m3 NB(1.2577)
## m9 NB(1.2598)
## m10 NB(1.4371)
## m11 0.08792 NB(1.2696)
## m12 0.01427 -1.057 NB(1.5443)
## init.theta df logLik AICc delta weight
## m0 1.22 3 -211.709 430.3 0.00 0.457
## m1 1.25 4 -211.273 432.1 1.81 0.185
## m4 1.35 5 -209.903 432.3 1.96 0.171
## m2 1.22 4 -211.691 433.0 2.64 0.122
## m3 1.26 5 -211.131 434.8 4.42 0.050
## m9 1.26 6 -211.101 437.9 7.51 0.011
## m10 1.44 8 -208.729 440.3 9.97 0.003
## m11 1.27 8 -210.961 444.8 14.44 0.000
## m12 1.54 11 -207.455 451.6 21.24 0.000
## Abbreviations:
## family: NB(1.2186) = 'Negative Binomial(1.2186)',
## NB(1.2198) = 'Negative Binomial(1.2198)',
## NB(1.2479) = 'Negative Binomial(1.2479)',
## NB(1.2577) = 'Negative Binomial(1.2577)',
## NB(1.2598) = 'Negative Binomial(1.2598)',
## NB(1.2696) = 'Negative Binomial(1.2696)',
## NB(1.346) = 'Negative Binomial(1.346)',
## NB(1.4371) = 'Negative Binomial(1.4371)',
## NB(1.5443) = 'Negative Binomial(1.5443)'
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 3.112000 0.5057
## m1 1.570000 0.7742 0.1568
## m4 -0.004181 1.1120 -0.013580 -1.4760
## m2 3.239000 0.4839 -0.063130
## m3 0.389700 0.9623 0.2772 0.09101
## m9 1.735000 0.7453 0.1612 -0.061480
## m10 -3.059000 1.6530 0.3025 0.009465 -1.7360
## m11 1.035000 0.8551 0.2451 -0.125000 0.06465
## m12 -8.376000 2.4980 0.9805 0.045930 0.47280 -0.9655
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m1
## m4
## m2
## m3
## m9 -0.18620
## m10 -0.16780 -0.2704
## m11 -0.07852
## m12 -0.16040 -1.7010
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik
## m0 2 -211.709
## m1 3 -211.278
## m4 4 -209.987
## m2 3 -211.691
## m3 4 -211.140
## m9 5 -211.110
## m10 7 -208.956
## m11 0.08787 7 -210.975
## m12 0.01388 -1.059 10 -207.910
## AICc delta weight
## m0 427.9 0.00 0.427
## m1 429.5 1.62 0.190
## m4 429.6 1.71 0.181
## m2 430.3 2.44 0.126
## m3 431.9 4.02 0.057
## m9 434.7 6.86 0.014
## m10 437.0 9.14 0.004
## m11 441.0 13.18 0.001
## m12 447.4 19.54 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 3.112000 0.5057
## m1 1.570000 0.7742 0.1568
## m4 -0.001833 1.1110 -0.013620 -1.4760
## m2 3.239000 0.4839 -0.063110
## m3 0.390600 0.9621 0.2772 0.09098
## m9 1.735000 0.7453 0.1612 -0.061460
## m10 -3.051000 1.6520 0.3023 0.009347 -1.7350
## m11 1.036000 0.8549 0.2450 -0.125000 0.06460
## m12 -8.351000 2.4940 0.9790 0.045340 0.47220 -0.9645
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m1
## m4
## m2
## m3
## m9 -0.18610
## m10 -0.16790 -0.2703
## m11 -0.07851
## m12 -0.16010 -1.6990
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 family
## m0 NB(1.2186)
## m1 NB(1.2479)
## m4 NB(1.346)
## m2 NB(1.2198)
## m3 NB(1.2577)
## m9 NB(1.2598)
## m10 NB(1.4371)
## m11 0.08792 NB(1.2696)
## m12 0.01427 -1.057 NB(1.5443)
## init.theta df logLik AICc delta weight
## m0 1.22 3 -211.709 430.3 0.00 0.457
## m1 1.25 4 -211.273 432.1 1.81 0.185
## m4 1.35 5 -209.903 432.3 1.96 0.171
## m2 1.22 4 -211.691 433.0 2.64 0.122
## m3 1.26 5 -211.131 434.8 4.42 0.050
## m9 1.26 6 -211.101 437.9 7.51 0.011
## m10 1.44 8 -208.729 440.3 9.97 0.003
## m11 1.27 8 -210.961 444.8 14.44 0.000
## m12 1.54 11 -207.455 451.6 21.24 0.000
## Abbreviations:
## family: NB(1.2186) = 'Negative Binomial(1.2186)',
## NB(1.2198) = 'Negative Binomial(1.2198)',
## NB(1.2479) = 'Negative Binomial(1.2479)',
## NB(1.2577) = 'Negative Binomial(1.2577)',
## NB(1.2598) = 'Negative Binomial(1.2598)',
## NB(1.2696) = 'Negative Binomial(1.2696)',
## NB(1.346) = 'Negative Binomial(1.346)',
## NB(1.4371) = 'Negative Binomial(1.4371)',
## NB(1.5443) = 'Negative Binomial(1.5443)'
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 3.112000 0.5057
## m1 1.570000 0.7742 0.1568
## m4 -0.004181 1.1120 -0.013580 -1.4760
## m2 3.239000 0.4839 -0.063130
## m3 0.389700 0.9623 0.2772 0.09101
## m9 1.735000 0.7453 0.1612 -0.061480
## m10 -3.059000 1.6530 0.3025 0.009465 -1.7360
## m11 1.035000 0.8551 0.2451 -0.125000 0.06465
## m12 -8.376000 2.4980 0.9805 0.045930 0.47280 -0.9655
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m1
## m4
## m2
## m3
## m9 -0.18620
## m10 -0.16780 -0.2704
## m11 -0.07852
## m12 -0.16040 -1.7010
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik
## m0 2 -211.709
## m1 3 -211.278
## m4 4 -209.987
## m2 3 -211.691
## m3 4 -211.140
## m9 5 -211.110
## m10 7 -208.956
## m11 0.08787 7 -210.975
## m12 0.01388 -1.059 10 -207.910
## AICc delta weight
## m0 427.9 0.00 0.427
## m1 429.5 1.62 0.190
## m4 429.6 1.71 0.181
## m2 430.3 2.44 0.126
## m3 431.9 4.02 0.057
## m9 434.7 6.86 0.014
## m10 437.0 9.14 0.004
## m11 441.0 13.18 0.001
## m12 447.4 19.54 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m2 -3.928 1.569 0.8042
## m0 -4.508 1.689
## m4 -2.919 1.337 0.8053 0.9706
## m1 -5.862 1.929 0.14290
## m9 -5.022 1.762 0.11350 0.8299
## m10 -7.717 2.161 1.23300 0.8354 0.9902
## m3 -5.838 1.927 0.12080 -0.01864
## m11 -4.653 1.705 0.04658 0.7618 -0.04919
## m12 -6.932 2.058 0.77310 0.7557 -0.29270 0.5907
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m2
## m0
## m4
## m1
## m9 0.1007
## m10 0.1525 -2.608
## m3
## m11 0.2062
## m12 0.2447 -1.668
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 family
## m2 NB(0.7018)
## m0 NB(0.6203)
## m4 NB(0.72)
## m1 NB(0.6262)
## m9 NB(0.7085)
## m10 NB(0.8599)
## m3 NB(0.6263)
## m11 0.08367 NB(0.7106)
## m12 0.08971 0.6076 NB(0.8785)
## init.theta df logLik AICc delta weight
## m2 0.702 4 -182.554 374.7 0.00 0.490
## m0 0.62 3 -184.783 376.5 1.78 0.201
## m4 0.72 5 -182.105 376.7 2.00 0.180
## m1 0.626 4 -184.610 378.8 4.11 0.063
## m9 0.709 6 -182.385 380.4 5.72 0.028
## m10 0.86 8 -178.992 380.8 6.13 0.023
## m3 0.626 5 -184.607 381.7 7.01 0.015
## m11 0.711 8 -182.333 387.5 12.82 0.001
## m12 0.878 11 -178.657 394.0 19.27 0.000
## Abbreviations:
## family: NB(0.6203) = 'Negative Binomial(0.6203)',
## NB(0.6262) = 'Negative Binomial(0.6262)',
## NB(0.6263) = 'Negative Binomial(0.6263)',
## NB(0.7018) = 'Negative Binomial(0.7018)',
## NB(0.7085) = 'Negative Binomial(0.7085)',
## NB(0.7106) = 'Negative Binomial(0.7106)',
## NB(0.72) = 'Negative Binomial(0.72)',
## NB(0.8599) = 'Negative Binomial(0.8599)',
## NB(0.8785) = 'Negative Binomial(0.8785)'
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m2 -3.927 1.569 0.8042
## m4 -2.920 1.337 0.8053 0.9705
## m0 -4.506 1.689
## m1 -5.860 1.928 0.14290
## m9 -5.022 1.762 0.11350 0.8299
## m10 -7.740 2.165 1.23600 0.8353 0.9906
## m3 -5.836 1.927 0.12080 -0.01862
## m11 -4.653 1.705 0.04658 0.7618 -0.04918
## m12 -6.954 2.062 0.77670 0.7557 -0.29040 0.5937
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m2
## m4
## m0
## m1
## m9 0.1007
## m10 0.1529 -2.613
## m3
## m11 0.2062
## m12 0.2450 -1.675
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik
## m2 3 -182.554
## m4 4 -182.110
## m0 2 -184.928
## m1 3 -184.732
## m9 5 -182.386
## m10 7 -179.321
## m3 4 -184.729
## m11 0.08363 7 -182.334
## m12 0.08964 0.6031 10 -179.053
## AICc delta weight
## m2 372.0 0.00 0.498
## m4 373.8 1.79 0.204
## m0 374.3 2.27 0.160
## m1 376.4 4.36 0.056
## m9 377.3 5.24 0.036
## m10 377.7 5.70 0.029
## m3 379.1 7.03 0.015
## m11 383.8 11.73 0.001
## m12 389.7 17.65 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m2 -3.928 1.569 0.8042
## m0 -4.508 1.689
## m4 -2.919 1.337 0.8053 0.9706
## m1 -5.862 1.929 0.14290
## m9 -5.022 1.762 0.11350 0.8299
## m10 -7.717 2.161 1.23300 0.8354 0.9902
## m3 -5.838 1.927 0.12080 -0.01864
## m11 -4.653 1.705 0.04658 0.7618 -0.04919
## m12 -6.932 2.058 0.77310 0.7557 -0.29270 0.5907
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m2
## m0
## m4
## m1
## m9 0.1007
## m10 0.1525 -2.608
## m3
## m11 0.2062
## m12 0.2447 -1.668
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 family
## m2 NB(0.7018)
## m0 NB(0.6203)
## m4 NB(0.72)
## m1 NB(0.6262)
## m9 NB(0.7085)
## m10 NB(0.8599)
## m3 NB(0.6263)
## m11 0.08367 NB(0.7106)
## m12 0.08971 0.6076 NB(0.8785)
## init.theta df logLik AICc delta weight
## m2 0.702 4 -182.554 374.7 0.00 0.490
## m0 0.62 3 -184.783 376.5 1.78 0.201
## m4 0.72 5 -182.105 376.7 2.00 0.180
## m1 0.626 4 -184.610 378.8 4.11 0.063
## m9 0.709 6 -182.385 380.4 5.72 0.028
## m10 0.86 8 -178.992 380.8 6.13 0.023
## m3 0.626 5 -184.607 381.7 7.01 0.015
## m11 0.711 8 -182.333 387.5 12.82 0.001
## m12 0.878 11 -178.657 394.0 19.27 0.000
## Abbreviations:
## family: NB(0.6203) = 'Negative Binomial(0.6203)',
## NB(0.6262) = 'Negative Binomial(0.6262)',
## NB(0.6263) = 'Negative Binomial(0.6263)',
## NB(0.7018) = 'Negative Binomial(0.7018)',
## NB(0.7085) = 'Negative Binomial(0.7085)',
## NB(0.7106) = 'Negative Binomial(0.7106)',
## NB(0.72) = 'Negative Binomial(0.72)',
## NB(0.8599) = 'Negative Binomial(0.8599)',
## NB(0.8785) = 'Negative Binomial(0.8785)'
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m2 -3.927 1.569 0.8042
## m4 -2.920 1.337 0.8053 0.9705
## m0 -4.506 1.689
## m1 -5.860 1.928 0.14290
## m9 -5.022 1.762 0.11350 0.8299
## m10 -7.740 2.165 1.23600 0.8353 0.9906
## m3 -5.836 1.927 0.12080 -0.01862
## m11 -4.653 1.705 0.04658 0.7618 -0.04918
## m12 -6.954 2.062 0.77670 0.7557 -0.29040 0.5937
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m2
## m4
## m0
## m1
## m9 0.1007
## m10 0.1529 -2.613
## m3
## m11 0.2062
## m12 0.2450 -1.675
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik
## m2 3 -182.554
## m4 4 -182.110
## m0 2 -184.928
## m1 3 -184.732
## m9 5 -182.386
## m10 7 -179.321
## m3 4 -184.729
## m11 0.08363 7 -182.334
## m12 0.08964 0.6031 10 -179.053
## AICc delta weight
## m2 372.0 0.00 0.498
## m4 373.8 1.79 0.204
## m0 374.3 2.27 0.160
## m1 376.4 4.36 0.056
## m9 377.3 5.24 0.036
## m10 377.7 5.70 0.029
## m3 379.1 7.03 0.015
## m11 383.8 11.73 0.001
## m12 389.7 17.65 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m2 -3.928 1.569 0.8042
## m0 -4.508 1.689
## m4 -2.919 1.337 0.8053 0.9706
## m1 -5.862 1.929 0.14290
## m9 -5.022 1.762 0.11350 0.8299
## m10 -7.717 2.161 1.23300 0.8354 0.9902
## m3 -5.838 1.927 0.12080 -0.01864
## m11 -4.653 1.705 0.04658 0.7618 -0.04919
## m12 -6.932 2.058 0.77310 0.7557 -0.29270 0.5907
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m2
## m0
## m4
## m1
## m9 0.1007
## m10 0.1525 -2.608
## m3
## m11 0.2062
## m12 0.2447 -1.668
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 family
## m2 NB(0.7018)
## m0 NB(0.6203)
## m4 NB(0.72)
## m1 NB(0.6262)
## m9 NB(0.7085)
## m10 NB(0.8599)
## m3 NB(0.6263)
## m11 0.08367 NB(0.7106)
## m12 0.08971 0.6076 NB(0.8785)
## init.theta df logLik AICc delta weight
## m2 0.702 4 -182.554 374.7 0.00 0.490
## m0 0.62 3 -184.783 376.5 1.78 0.201
## m4 0.72 5 -182.105 376.7 2.00 0.180
## m1 0.626 4 -184.610 378.8 4.11 0.063
## m9 0.709 6 -182.385 380.4 5.72 0.028
## m10 0.86 8 -178.992 380.8 6.13 0.023
## m3 0.626 5 -184.607 381.7 7.01 0.015
## m11 0.711 8 -182.333 387.5 12.82 0.001
## m12 0.878 11 -178.657 394.0 19.27 0.000
## Abbreviations:
## family: NB(0.6203) = 'Negative Binomial(0.6203)',
## NB(0.6262) = 'Negative Binomial(0.6262)',
## NB(0.6263) = 'Negative Binomial(0.6263)',
## NB(0.7018) = 'Negative Binomial(0.7018)',
## NB(0.7085) = 'Negative Binomial(0.7085)',
## NB(0.7106) = 'Negative Binomial(0.7106)',
## NB(0.72) = 'Negative Binomial(0.72)',
## NB(0.8599) = 'Negative Binomial(0.8599)',
## NB(0.8785) = 'Negative Binomial(0.8785)'
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m2 -3.927 1.569 0.8042
## m4 -2.920 1.337 0.8053 0.9705
## m0 -4.506 1.689
## m1 -5.860 1.928 0.14290
## m9 -5.022 1.762 0.11350 0.8299
## m10 -7.740 2.165 1.23600 0.8353 0.9906
## m3 -5.836 1.927 0.12080 -0.01862
## m11 -4.653 1.705 0.04658 0.7618 -0.04918
## m12 -6.954 2.062 0.77670 0.7557 -0.29040 0.5937
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m2
## m4
## m0
## m1
## m9 0.1007
## m10 0.1529 -2.613
## m3
## m11 0.2062
## m12 0.2450 -1.675
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik
## m2 3 -182.554
## m4 4 -182.110
## m0 2 -184.928
## m1 3 -184.732
## m9 5 -182.386
## m10 7 -179.321
## m3 4 -184.729
## m11 0.08363 7 -182.334
## m12 0.08964 0.6031 10 -179.053
## AICc delta weight
## m2 372.0 0.00 0.498
## m4 373.8 1.79 0.204
## m0 374.3 2.27 0.160
## m1 376.4 4.36 0.056
## m9 377.3 5.24 0.036
## m10 377.7 5.70 0.029
## m3 379.1 7.03 0.015
## m11 383.8 11.73 0.001
## m12 389.7 17.65 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m2 -3.928 1.569 0.8042
## m0 -4.508 1.689
## m4 -2.919 1.337 0.8053 0.9706
## m1 -5.862 1.929 0.14290
## m9 -5.022 1.762 0.11350 0.8299
## m10 -7.717 2.161 1.23300 0.8354 0.9902
## m3 -5.838 1.927 0.12080 -0.01864
## m11 -4.653 1.705 0.04658 0.7618 -0.04919
## m12 -6.932 2.058 0.77310 0.7557 -0.29270 0.5907
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m2
## m0
## m4
## m1
## m9 0.1007
## m10 0.1525 -2.608
## m3
## m11 0.2062
## m12 0.2447 -1.668
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 family
## m2 NB(0.7018)
## m0 NB(0.6203)
## m4 NB(0.72)
## m1 NB(0.6262)
## m9 NB(0.7085)
## m10 NB(0.8599)
## m3 NB(0.6263)
## m11 0.08367 NB(0.7106)
## m12 0.08971 0.6076 NB(0.8785)
## init.theta df logLik AICc delta weight
## m2 0.702 4 -182.554 374.7 0.00 0.490
## m0 0.62 3 -184.783 376.5 1.78 0.201
## m4 0.72 5 -182.105 376.7 2.00 0.180
## m1 0.626 4 -184.610 378.8 4.11 0.063
## m9 0.709 6 -182.385 380.4 5.72 0.028
## m10 0.86 8 -178.992 380.8 6.13 0.023
## m3 0.626 5 -184.607 381.7 7.01 0.015
## m11 0.711 8 -182.333 387.5 12.82 0.001
## m12 0.878 11 -178.657 394.0 19.27 0.000
## Abbreviations:
## family: NB(0.6203) = 'Negative Binomial(0.6203)',
## NB(0.6262) = 'Negative Binomial(0.6262)',
## NB(0.6263) = 'Negative Binomial(0.6263)',
## NB(0.7018) = 'Negative Binomial(0.7018)',
## NB(0.7085) = 'Negative Binomial(0.7085)',
## NB(0.7106) = 'Negative Binomial(0.7106)',
## NB(0.72) = 'Negative Binomial(0.72)',
## NB(0.8599) = 'Negative Binomial(0.8599)',
## NB(0.8785) = 'Negative Binomial(0.8785)'
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m2 -3.927 1.569 0.8042
## m4 -2.920 1.337 0.8053 0.9705
## m0 -4.506 1.689
## m1 -5.860 1.928 0.14290
## m9 -5.022 1.762 0.11350 0.8299
## m10 -7.740 2.165 1.23600 0.8353 0.9906
## m3 -5.836 1.927 0.12080 -0.01862
## m11 -4.653 1.705 0.04658 0.7618 -0.04918
## m12 -6.954 2.062 0.77670 0.7557 -0.29040 0.5937
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m2
## m4
## m0
## m1
## m9 0.1007
## m10 0.1529 -2.613
## m3
## m11 0.2062
## m12 0.2450 -1.675
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik
## m2 3 -182.554
## m4 4 -182.110
## m0 2 -184.928
## m1 3 -184.732
## m9 5 -182.386
## m10 7 -179.321
## m3 4 -184.729
## m11 0.08363 7 -182.334
## m12 0.08964 0.6031 10 -179.053
## AICc delta weight
## m2 372.0 0.00 0.498
## m4 373.8 1.79 0.204
## m0 374.3 2.27 0.160
## m1 376.4 4.36 0.056
## m9 377.3 5.24 0.036
## m10 377.7 5.70 0.029
## m3 379.1 7.03 0.015
## m11 383.8 11.73 0.001
## m12 389.7 17.65 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 17.18 -1.651
## m1 18.17 -1.793 0.04836
## m2 18.05 -1.777 0.04946
## m3 18.43 -1.827 0.02240 -0.02788
## m4 18.29 -1.809 0.05174 -0.09294
## m9 22.02 -2.352 0.09093 0.15890
## m11 23.42 -2.552 0.06307 0.37020 -0.03465
## m10 22.46 -2.408 0.11040 0.16210 -0.17500
## m12 21.88 -2.334 0.18410 0.33650 0.07449 0.10760
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m1
## m2
## m3
## m4
## m9 0.3369
## m11 0.1189
## m10 0.3351 -0.06348
## m12 0.1249 -0.39250
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 family
## m0 NB(2.2352)
## m1 NB(2.2453)
## m2 NB(2.2379)
## m3 NB(2.2495)
## m4 NB(2.2398)
## m9 NB(2.4013)
## m11 -0.2396 NB(2.5332)
## m10 NB(2.4093)
## m12 -0.2217 -0.3402 NB(2.5862)
## init.theta df logLik AICc delta weight
## m0 2.24 3 -199.129 405.2 0.00 0.578
## m1 2.25 4 -199.052 407.7 2.52 0.164
## m2 2.24 4 -199.108 407.8 2.63 0.155
## m3 2.25 5 -199.021 410.5 5.36 0.040
## m4 2.24 5 -199.094 410.7 5.51 0.037
## m9 2.4 6 -197.911 411.5 6.29 0.025
## m11 2.53 8 -197.012 416.9 11.70 0.002
## m10 2.41 8 -197.856 418.6 13.39 0.001
## m12 2.59 11 -196.668 430.0 24.82 0.000
## Abbreviations:
## family: NB(2.2352) = 'Negative Binomial(2.2352)',
## NB(2.2379) = 'Negative Binomial(2.2379)',
## NB(2.2398) = 'Negative Binomial(2.2398)',
## NB(2.2453) = 'Negative Binomial(2.2453)',
## NB(2.2495) = 'Negative Binomial(2.2495)',
## NB(2.4013) = 'Negative Binomial(2.4013)',
## NB(2.4093) = 'Negative Binomial(2.4093)',
## NB(2.5332) = 'Negative Binomial(2.5332)',
## NB(2.5862) = 'Negative Binomial(2.5862)'
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 17.18 -1.651
## m1 18.17 -1.794 0.04837
## m2 18.05 -1.777 0.04943
## m3 18.43 -1.827 0.02241 -0.02788
## m4 18.29 -1.808 0.05172 -0.09296
## m9 22.02 -2.353 0.09096 0.15890
## m11 23.44 -2.554 0.06315 0.37040 -0.03462
## m10 22.47 -2.409 0.11040 0.16220 -0.17490
## m12 21.89 -2.336 0.18410 0.33670 0.07447 0.10770
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m1
## m2
## m3
## m4
## m9 0.3369
## m11 0.1191
## m10 0.3352 -0.06337
## m12 0.1251 -0.39220
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik
## m0 2 -199.129
## m1 3 -199.052
## m2 3 -199.108
## m3 4 -199.021
## m4 4 -199.094
## m9 5 -197.950
## m11 -0.2396 7 -197.133
## m10 7 -197.900
## m12 -0.2217 -0.3401 10 -196.832
## AICc delta weight
## m0 402.7 0.00 0.544
## m1 405.0 2.32 0.170
## m2 405.1 2.44 0.161
## m3 407.6 4.94 0.046
## m4 407.8 5.08 0.043
## m9 408.4 5.70 0.032
## m11 413.4 10.65 0.003
## m10 414.9 12.19 0.001
## m12 425.2 22.54 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 17.18 -1.651
## m1 18.17 -1.793 0.04836
## m2 18.05 -1.777 0.04946
## m3 18.43 -1.827 0.02240 -0.02788
## m4 18.29 -1.809 0.05174 -0.09294
## m9 22.02 -2.352 0.09093 0.15890
## m11 23.42 -2.552 0.06307 0.37020 -0.03465
## m10 22.46 -2.408 0.11040 0.16210 -0.17500
## m12 21.88 -2.334 0.18410 0.33650 0.07449 0.10760
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m1
## m2
## m3
## m4
## m9 0.3369
## m11 0.1189
## m10 0.3351 -0.06348
## m12 0.1249 -0.39250
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 family
## m0 NB(2.2352)
## m1 NB(2.2453)
## m2 NB(2.2379)
## m3 NB(2.2495)
## m4 NB(2.2398)
## m9 NB(2.4013)
## m11 -0.2396 NB(2.5332)
## m10 NB(2.4093)
## m12 -0.2217 -0.3402 NB(2.5862)
## init.theta df logLik AICc delta weight
## m0 2.24 3 -199.129 405.2 0.00 0.578
## m1 2.25 4 -199.052 407.7 2.52 0.164
## m2 2.24 4 -199.108 407.8 2.63 0.155
## m3 2.25 5 -199.021 410.5 5.36 0.040
## m4 2.24 5 -199.094 410.7 5.51 0.037
## m9 2.4 6 -197.911 411.5 6.29 0.025
## m11 2.53 8 -197.012 416.9 11.70 0.002
## m10 2.41 8 -197.856 418.6 13.39 0.001
## m12 2.59 11 -196.668 430.0 24.82 0.000
## Abbreviations:
## family: NB(2.2352) = 'Negative Binomial(2.2352)',
## NB(2.2379) = 'Negative Binomial(2.2379)',
## NB(2.2398) = 'Negative Binomial(2.2398)',
## NB(2.2453) = 'Negative Binomial(2.2453)',
## NB(2.2495) = 'Negative Binomial(2.2495)',
## NB(2.4013) = 'Negative Binomial(2.4013)',
## NB(2.4093) = 'Negative Binomial(2.4093)',
## NB(2.5332) = 'Negative Binomial(2.5332)',
## NB(2.5862) = 'Negative Binomial(2.5862)'
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 17.18 -1.651
## m1 18.17 -1.794 0.04837
## m2 18.05 -1.777 0.04943
## m3 18.43 -1.827 0.02241 -0.02788
## m4 18.29 -1.808 0.05172 -0.09296
## m9 22.02 -2.353 0.09096 0.15890
## m11 23.44 -2.554 0.06315 0.37040 -0.03462
## m10 22.47 -2.409 0.11040 0.16220 -0.17490
## m12 21.89 -2.336 0.18410 0.33670 0.07447 0.10770
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m1
## m2
## m3
## m4
## m9 0.3369
## m11 0.1191
## m10 0.3352 -0.06337
## m12 0.1251 -0.39220
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik
## m0 2 -199.129
## m1 3 -199.052
## m2 3 -199.108
## m3 4 -199.021
## m4 4 -199.094
## m9 5 -197.950
## m11 -0.2396 7 -197.133
## m10 7 -197.900
## m12 -0.2217 -0.3401 10 -196.832
## AICc delta weight
## m0 402.7 0.00 0.544
## m1 405.0 2.32 0.170
## m2 405.1 2.44 0.161
## m3 407.6 4.94 0.046
## m4 407.8 5.08 0.043
## m9 408.4 5.70 0.032
## m11 413.4 10.65 0.003
## m10 414.9 12.19 0.001
## m12 425.2 22.54 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 17.18 -1.651
## m1 18.17 -1.793 0.04836
## m2 18.05 -1.777 0.04946
## m3 18.43 -1.827 0.02240 -0.02788
## m4 18.29 -1.809 0.05174 -0.09294
## m9 22.02 -2.352 0.09093 0.15890
## m11 23.42 -2.552 0.06307 0.37020 -0.03465
## m10 22.46 -2.408 0.11040 0.16210 -0.17500
## m12 21.88 -2.334 0.18410 0.33650 0.07449 0.10760
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m1
## m2
## m3
## m4
## m9 0.3369
## m11 0.1189
## m10 0.3351 -0.06348
## m12 0.1249 -0.39250
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 family
## m0 NB(2.2352)
## m1 NB(2.2453)
## m2 NB(2.2379)
## m3 NB(2.2495)
## m4 NB(2.2398)
## m9 NB(2.4013)
## m11 -0.2396 NB(2.5332)
## m10 NB(2.4093)
## m12 -0.2217 -0.3402 NB(2.5862)
## init.theta df logLik AICc delta weight
## m0 2.24 3 -199.129 405.2 0.00 0.578
## m1 2.25 4 -199.052 407.7 2.52 0.164
## m2 2.24 4 -199.108 407.8 2.63 0.155
## m3 2.25 5 -199.021 410.5 5.36 0.040
## m4 2.24 5 -199.094 410.7 5.51 0.037
## m9 2.4 6 -197.911 411.5 6.29 0.025
## m11 2.53 8 -197.012 416.9 11.70 0.002
## m10 2.41 8 -197.856 418.6 13.39 0.001
## m12 2.59 11 -196.668 430.0 24.82 0.000
## Abbreviations:
## family: NB(2.2352) = 'Negative Binomial(2.2352)',
## NB(2.2379) = 'Negative Binomial(2.2379)',
## NB(2.2398) = 'Negative Binomial(2.2398)',
## NB(2.2453) = 'Negative Binomial(2.2453)',
## NB(2.2495) = 'Negative Binomial(2.2495)',
## NB(2.4013) = 'Negative Binomial(2.4013)',
## NB(2.4093) = 'Negative Binomial(2.4093)',
## NB(2.5332) = 'Negative Binomial(2.5332)',
## NB(2.5862) = 'Negative Binomial(2.5862)'
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 17.18 -1.651
## m1 18.17 -1.794 0.04837
## m2 18.05 -1.777 0.04943
## m3 18.43 -1.827 0.02241 -0.02788
## m4 18.29 -1.808 0.05172 -0.09296
## m9 22.02 -2.353 0.09096 0.15890
## m11 23.44 -2.554 0.06315 0.37040 -0.03462
## m10 22.47 -2.409 0.11040 0.16220 -0.17490
## m12 21.89 -2.336 0.18410 0.33670 0.07447 0.10770
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m1
## m2
## m3
## m4
## m9 0.3369
## m11 0.1191
## m10 0.3352 -0.06337
## m12 0.1251 -0.39220
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik
## m0 2 -199.129
## m1 3 -199.052
## m2 3 -199.108
## m3 4 -199.021
## m4 4 -199.094
## m9 5 -197.950
## m11 -0.2396 7 -197.133
## m10 7 -197.900
## m12 -0.2217 -0.3401 10 -196.832
## AICc delta weight
## m0 402.7 0.00 0.544
## m1 405.0 2.32 0.170
## m2 405.1 2.44 0.161
## m3 407.6 4.94 0.046
## m4 407.8 5.08 0.043
## m9 408.4 5.70 0.032
## m11 413.4 10.65 0.003
## m10 414.9 12.19 0.001
## m12 425.2 22.54 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 3.341 0.6655
## m1 3.736 0.5961 0.10370
## m2 2.404 0.8376 0.2046
## m4 3.196 0.7156 0.1820 -0.4256
## m3 3.692 0.5939 0.17410 0.06079
## m9 3.031 0.7249 0.09785 0.1968
## m10 3.666 0.6323 0.04671 0.1811 -0.4300
## m11 3.011 0.7190 0.16110 0.2867 0.05421
## m12 3.675 0.6082 0.20900 0.2803 0.13060 -0.1693
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m1
## m2
## m4
## m3
## m9 0.13030
## m10 0.13900 0.2023
## m11 0.02743
## m12 0.01629 -0.1480
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 family
## m0 NB(3.1657)
## m1 NB(3.2803)
## m2 NB(3.2646)
## m4 NB(3.351)
## m3 NB(3.3246)
## m9 NB(3.4139)
## m10 NB(3.5742)
## m11 -0.09877 NB(3.4971)
## m12 -0.11430 -0.2888 NB(3.7207)
## init.theta df logLik AICc delta weight
## m0 3.17 3 -230.221 467.4 0.00 0.445
## m1 3.28 4 -229.636 468.9 1.51 0.209
## m2 3.26 4 -229.715 469.0 1.66 0.194
## m4 3.35 5 -229.287 471.1 3.71 0.070
## m3 3.32 5 -229.416 471.3 3.97 0.061
## m9 3.41 6 -228.983 473.6 6.25 0.020
## m10 3.57 8 -228.235 479.3 11.96 0.001
## m11 3.5 8 -228.589 480.0 12.67 0.001
## m12 3.72 11 -227.581 491.8 24.47 0.000
## Abbreviations:
## family: NB(3.1657) = 'Negative Binomial(3.1657)',
## NB(3.2646) = 'Negative Binomial(3.2646)',
## NB(3.2803) = 'Negative Binomial(3.2803)',
## NB(3.3246) = 'Negative Binomial(3.3246)',
## NB(3.351) = 'Negative Binomial(3.351)',
## NB(3.4139) = 'Negative Binomial(3.4139)',
## NB(3.4971) = 'Negative Binomial(3.4971)',
## NB(3.5742) = 'Negative Binomial(3.5742)',
## NB(3.7207) = 'Negative Binomial(3.7207)'
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 3.341 0.6655
## m1 3.736 0.5961 0.10370
## m2 2.404 0.8376 0.2046
## m4 3.197 0.7156 0.1820 -0.4256
## m3 3.692 0.5939 0.17410 0.06079
## m9 3.031 0.7249 0.09785 0.1968
## m10 3.666 0.6323 0.04671 0.1811 -0.4300
## m11 3.011 0.7190 0.16110 0.2867 0.05421
## m12 3.675 0.6082 0.20900 0.2804 0.13060 -0.1692
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m1
## m2
## m4
## m3
## m9 0.13030
## m10 0.13900 0.2023
## m11 0.02743
## m12 0.01628 -0.1481
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik
## m0 2 -230.221
## m1 3 -229.646
## m2 3 -229.722
## m4 4 -229.312
## m3 4 -229.435
## m9 5 -229.027
## m10 7 -228.347
## m11 -0.09877 7 -228.665
## m12 -0.11430 -0.2888 10 -227.779
## AICc delta weight
## m0 464.9 0.00 0.414
## m1 466.2 1.33 0.213
## m2 466.4 1.48 0.198
## m4 468.2 3.34 0.078
## m3 468.5 3.58 0.069
## m9 470.6 5.67 0.024
## m10 475.8 10.90 0.002
## m11 476.4 11.54 0.001
## m12 487.1 22.25 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 3.341 0.6655
## m1 3.736 0.5961 0.10370
## m2 2.404 0.8376 0.2046
## m4 3.196 0.7156 0.1820 -0.4256
## m3 3.692 0.5939 0.17410 0.06079
## m9 3.031 0.7249 0.09785 0.1968
## m10 3.666 0.6323 0.04671 0.1811 -0.4300
## m11 3.011 0.7190 0.16110 0.2867 0.05421
## m12 3.675 0.6082 0.20900 0.2803 0.13060 -0.1693
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m1
## m2
## m4
## m3
## m9 0.13030
## m10 0.13900 0.2023
## m11 0.02743
## m12 0.01629 -0.1480
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 family
## m0 NB(3.1657)
## m1 NB(3.2803)
## m2 NB(3.2646)
## m4 NB(3.351)
## m3 NB(3.3246)
## m9 NB(3.4139)
## m10 NB(3.5742)
## m11 -0.09877 NB(3.4971)
## m12 -0.11430 -0.2888 NB(3.7207)
## init.theta df logLik AICc delta weight
## m0 3.17 3 -230.221 467.4 0.00 0.445
## m1 3.28 4 -229.636 468.9 1.51 0.209
## m2 3.26 4 -229.715 469.0 1.66 0.194
## m4 3.35 5 -229.287 471.1 3.71 0.070
## m3 3.32 5 -229.416 471.3 3.97 0.061
## m9 3.41 6 -228.983 473.6 6.25 0.020
## m10 3.57 8 -228.235 479.3 11.96 0.001
## m11 3.5 8 -228.589 480.0 12.67 0.001
## m12 3.72 11 -227.581 491.8 24.47 0.000
## Abbreviations:
## family: NB(3.1657) = 'Negative Binomial(3.1657)',
## NB(3.2646) = 'Negative Binomial(3.2646)',
## NB(3.2803) = 'Negative Binomial(3.2803)',
## NB(3.3246) = 'Negative Binomial(3.3246)',
## NB(3.351) = 'Negative Binomial(3.351)',
## NB(3.4139) = 'Negative Binomial(3.4139)',
## NB(3.4971) = 'Negative Binomial(3.4971)',
## NB(3.5742) = 'Negative Binomial(3.5742)',
## NB(3.7207) = 'Negative Binomial(3.7207)'
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 3.341 0.6655
## m1 3.736 0.5961 0.10370
## m2 2.404 0.8376 0.2046
## m4 3.197 0.7156 0.1820 -0.4256
## m3 3.692 0.5939 0.17410 0.06079
## m9 3.031 0.7249 0.09785 0.1968
## m10 3.666 0.6323 0.04671 0.1811 -0.4300
## m11 3.011 0.7190 0.16110 0.2867 0.05421
## m12 3.675 0.6082 0.20900 0.2804 0.13060 -0.1692
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m1
## m2
## m4
## m3
## m9 0.13030
## m10 0.13900 0.2023
## m11 0.02743
## m12 0.01628 -0.1481
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik
## m0 2 -230.221
## m1 3 -229.646
## m2 3 -229.722
## m4 4 -229.312
## m3 4 -229.435
## m9 5 -229.027
## m10 7 -228.347
## m11 -0.09877 7 -228.665
## m12 -0.11430 -0.2888 10 -227.779
## AICc delta weight
## m0 464.9 0.00 0.414
## m1 466.2 1.33 0.213
## m2 466.4 1.48 0.198
## m4 468.2 3.34 0.078
## m3 468.5 3.58 0.069
## m9 470.6 5.67 0.024
## m10 475.8 10.90 0.002
## m11 476.4 11.54 0.001
## m12 487.1 22.25 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 -4.087 1.578
## m1 -4.358 1.611 -0.2741
## m3 -4.305 1.631 -0.5126 -0.2069
## m2 -4.153 1.591 -0.023200
## m4 -3.911 1.475 -0.016420 1.0260
## m9 -5.006 1.736 -0.2750 0.007379
## m12 -7.433 2.223 -1.6700 0.024060 -1.0540 0.2233
## m11 -4.577 1.684 -0.5138 -0.031980 -0.2077
## m10 -4.215 1.511 -0.3719 0.002815 1.0610
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m1
## m3
## m2
## m4
## m9 0.08088
## m12 0.13560 2.7460
## m11 0.10770
## m10 0.04747 0.2277
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 family
## m0 NB(1.1672)
## m1 NB(1.2554)
## m3 NB(1.3436)
## m2 NB(1.1674)
## m4 NB(1.2328)
## m9 NB(1.258)
## m12 -0.01059 2.047 NB(2.0235)
## m11 0.04327 NB(1.3475)
## m10 NB(1.331)
## init.theta df logLik AICc delta weight
## m0 1.17 3 -151.610 310.1 0.00 0.340
## m1 1.26 4 -150.413 310.4 0.28 0.295
## m3 1.34 5 -149.322 311.1 1.00 0.206
## m2 1.17 4 -151.607 312.8 2.67 0.089
## m4 1.23 5 -150.724 313.9 3.81 0.051
## m9 1.26 6 -150.378 316.4 6.27 0.015
## m12 2.02 11 -142.175 321.0 10.87 0.001
## m11 1.35 8 -149.274 321.4 11.26 0.001
## m10 1.33 8 -149.466 321.8 11.65 0.001
## Abbreviations:
## family: NB(1.1672) = 'Negative Binomial(1.1672)',
## NB(1.1674) = 'Negative Binomial(1.1674)',
## NB(1.2328) = 'Negative Binomial(1.2328)',
## NB(1.2554) = 'Negative Binomial(1.2554)',
## NB(1.258) = 'Negative Binomial(1.258)',
## NB(1.331) = 'Negative Binomial(1.331)',
## NB(1.3436) = 'Negative Binomial(1.3436)',
## NB(1.3475) = 'Negative Binomial(1.3475)',
## NB(2.0235) = 'Negative Binomial(2.0235)'
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 -4.087 1.578
## m1 -4.363 1.612 -0.2744
## m3 -4.322 1.634 -0.5132 -0.2070
## m2 -4.152 1.591 -0.023220
## m4 -3.911 1.475 -0.016520 1.0260
## m9 -5.013 1.738 -0.2752 0.007349
## m11 -4.614 1.691 -0.5145 -0.031460 -0.2078
## m10 -4.225 1.513 -0.3762 0.002751 1.0640
## m12 -7.976 2.330 -1.7390 0.032300 -1.0900 0.2025
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m1
## m3
## m2
## m4
## m9 0.08111
## m11 0.10800
## m10 0.04770 0.2366
## m12 0.14060 2.8890
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik
## m0 2 -151.610
## m1 3 -150.451
## m3 4 -149.465
## m2 3 -151.607
## m4 4 -150.746
## m9 5 -150.419
## m11 0.04252 7 -149.422
## m10 7 -149.590
## m12 -0.02024 2.124 10 -144.102
## AICc delta weight
## m0 307.7 0.00 0.321
## m1 307.8 0.16 0.296
## m3 308.5 0.86 0.208
## m2 310.1 2.47 0.093
## m4 311.1 3.43 0.058
## m9 313.3 5.67 0.019
## m11 317.9 10.27 0.002
## m10 318.3 10.61 0.002
## m12 319.8 12.12 0.001
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) cv.dpt prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m1 3.894 0.57010 -0.02447
## m3 4.021 0.44880 -4.764
## m5 6.279 0.03329 -0.09525
## m6 1.774 0.83170
## m4 3.345 0.24940 0.03446
## m2 2.759 0.44200 3.685
## m0 3.112 0.50570
## lst_wet family init.theta df logLik AICc delta weight
## m1 NB(2.3496) 2.35 4 -200.201 410.0 0.00 0.543
## m3 NB(2.3183) 2.32 4 -200.464 410.5 0.53 0.417
## m5 NB(1.988) 1.99 4 -202.964 415.5 5.53 0.034
## m6 -0.0512 NB(1.8048) 1.8 4 -204.735 419.1 9.07 0.006
## m4 NB(1.5004) 1.5 4 -207.966 425.5 15.53 0.000
## m2 NB(1.3551) 1.36 4 -209.783 429.2 19.16 0.000
## m0 NB(1.2186) 1.22 3 -211.709 430.3 20.34 0.000
## Abbreviations:
## family: NB(1.2186) = 'Negative Binomial(1.2186)',
## NB(1.3551) = 'Negative Binomial(1.3551)',
## NB(1.5004) = 'Negative Binomial(1.5004)',
## NB(1.8048) = 'Negative Binomial(1.8048)',
## NB(1.988) = 'Negative Binomial(1.988)',
## NB(2.3183) = 'Negative Binomial(2.3183)',
## NB(2.3496) = 'Negative Binomial(2.3496)'
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m1 3.895 -0.02447 0.57010
## m3 4.022 0.44860 -4.763
## m5 6.268 0.03512 -0.09511
## m6 1.790 0.82880
## m4 3.355 0.24820 0.0344
## m2 2.764 0.44130 3.681
## lst_wet df logLik AICc delta weight
## m1 3 -200.201 407.3 0.00 0.549
## m3 3 -200.466 407.9 0.53 0.421
## m5 3 -203.210 413.3 6.02 0.027
## m6 -0.05109 3 -205.367 417.7 10.33 0.003
## m4 3 -209.957 426.8 19.51 0.000
## m2 3 -212.937 432.8 25.47 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) cv.dpt prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m1 3.894 0.57010 -0.02447
## m3 4.021 0.44880 -4.764
## m5 6.279 0.03329 -0.09525
## m6 1.774 0.83170
## m4 3.345 0.24940 0.03446
## m2 2.759 0.44200 3.685
## m0 3.112 0.50570
## lst_wet family init.theta df logLik AICc delta weight
## m1 NB(2.3496) 2.35 4 -200.201 410.0 0.00 0.543
## m3 NB(2.3183) 2.32 4 -200.464 410.5 0.53 0.417
## m5 NB(1.988) 1.99 4 -202.964 415.5 5.53 0.034
## m6 -0.0512 NB(1.8048) 1.8 4 -204.735 419.1 9.07 0.006
## m4 NB(1.5004) 1.5 4 -207.966 425.5 15.53 0.000
## m2 NB(1.3551) 1.36 4 -209.783 429.2 19.16 0.000
## m0 NB(1.2186) 1.22 3 -211.709 430.3 20.34 0.000
## Abbreviations:
## family: NB(1.2186) = 'Negative Binomial(1.2186)',
## NB(1.3551) = 'Negative Binomial(1.3551)',
## NB(1.5004) = 'Negative Binomial(1.5004)',
## NB(1.8048) = 'Negative Binomial(1.8048)',
## NB(1.988) = 'Negative Binomial(1.988)',
## NB(2.3183) = 'Negative Binomial(2.3183)',
## NB(2.3496) = 'Negative Binomial(2.3496)'
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m1 3.895 -0.02447 0.57010
## m3 4.022 0.44860 -4.763
## m5 6.268 0.03512 -0.09511
## m6 1.790 0.82880
## m4 3.355 0.24820 0.0344
## m2 2.764 0.44130 3.681
## lst_wet df logLik AICc delta weight
## m1 3 -200.201 407.3 0.00 0.549
## m3 3 -200.466 407.9 0.53 0.421
## m5 3 -203.210 413.3 6.02 0.027
## m6 -0.05109 3 -205.367 417.7 10.33 0.003
## m4 3 -209.957 426.8 19.51 0.000
## m2 3 -212.937 432.8 25.47 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) cv.dpt prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m1 3.894 0.57010 -0.02447
## m3 4.021 0.44880 -4.764
## m5 6.279 0.03329 -0.09525
## m6 1.774 0.83170
## m4 3.345 0.24940 0.03446
## m2 2.759 0.44200 3.685
## m0 3.112 0.50570
## lst_wet family init.theta df logLik AICc delta weight
## m1 NB(2.3496) 2.35 4 -200.201 410.0 0.00 0.543
## m3 NB(2.3183) 2.32 4 -200.464 410.5 0.53 0.417
## m5 NB(1.988) 1.99 4 -202.964 415.5 5.53 0.034
## m6 -0.0512 NB(1.8048) 1.8 4 -204.735 419.1 9.07 0.006
## m4 NB(1.5004) 1.5 4 -207.966 425.5 15.53 0.000
## m2 NB(1.3551) 1.36 4 -209.783 429.2 19.16 0.000
## m0 NB(1.2186) 1.22 3 -211.709 430.3 20.34 0.000
## Abbreviations:
## family: NB(1.2186) = 'Negative Binomial(1.2186)',
## NB(1.3551) = 'Negative Binomial(1.3551)',
## NB(1.5004) = 'Negative Binomial(1.5004)',
## NB(1.8048) = 'Negative Binomial(1.8048)',
## NB(1.988) = 'Negative Binomial(1.988)',
## NB(2.3183) = 'Negative Binomial(2.3183)',
## NB(2.3496) = 'Negative Binomial(2.3496)'
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m1 3.895 -0.02447 0.57010
## m3 4.022 0.44860 -4.763
## m5 6.268 0.03512 -0.09511
## m6 1.790 0.82880
## m4 3.355 0.24820 0.0344
## m2 2.764 0.44130 3.681
## lst_wet df logLik AICc delta weight
## m1 3 -200.201 407.3 0.00 0.549
## m3 3 -200.466 407.9 0.53 0.421
## m5 3 -203.210 413.3 6.02 0.027
## m6 -0.05109 3 -205.367 417.7 10.33 0.003
## m4 3 -209.957 426.8 19.51 0.000
## m2 3 -212.937 432.8 25.47 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) cv.dpt prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m1 3.894 0.57010 -0.02447
## m3 4.021 0.44880 -4.764
## m5 6.279 0.03329 -0.09525
## m6 1.774 0.83170
## m4 3.345 0.24940 0.03446
## m2 2.759 0.44200 3.685
## m0 3.112 0.50570
## lst_wet family init.theta df logLik AICc delta weight
## m1 NB(2.3496) 2.35 4 -200.201 410.0 0.00 0.543
## m3 NB(2.3183) 2.32 4 -200.464 410.5 0.53 0.417
## m5 NB(1.988) 1.99 4 -202.964 415.5 5.53 0.034
## m6 -0.0512 NB(1.8048) 1.8 4 -204.735 419.1 9.07 0.006
## m4 NB(1.5004) 1.5 4 -207.966 425.5 15.53 0.000
## m2 NB(1.3551) 1.36 4 -209.783 429.2 19.16 0.000
## m0 NB(1.2186) 1.22 3 -211.709 430.3 20.34 0.000
## Abbreviations:
## family: NB(1.2186) = 'Negative Binomial(1.2186)',
## NB(1.3551) = 'Negative Binomial(1.3551)',
## NB(1.5004) = 'Negative Binomial(1.5004)',
## NB(1.8048) = 'Negative Binomial(1.8048)',
## NB(1.988) = 'Negative Binomial(1.988)',
## NB(2.3183) = 'Negative Binomial(2.3183)',
## NB(2.3496) = 'Negative Binomial(2.3496)'
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m1 3.895 -0.02447 0.57010
## m3 4.022 0.44860 -4.763
## m5 6.268 0.03512 -0.09511
## m6 1.790 0.82880
## m4 3.355 0.24820 0.0344
## m2 2.764 0.44130 3.681
## lst_wet df logLik AICc delta weight
## m1 3 -200.201 407.3 0.00 0.549
## m3 3 -200.466 407.9 0.53 0.421
## m5 3 -203.210 413.3 6.02 0.027
## m6 -0.05109 3 -205.367 417.7 10.33 0.003
## m4 3 -209.957 426.8 19.51 0.000
## m2 3 -212.937 432.8 25.47 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) cv.dpt prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m1 3.894 0.57010 -0.02447
## m3 4.021 0.44880 -4.764
## m5 6.279 0.03329 -0.09525
## m6 1.774 0.83170
## m4 3.345 0.24940 0.03446
## m2 2.759 0.44200 3.685
## m0 3.112 0.50570
## lst_wet family init.theta df logLik AICc delta weight
## m1 NB(2.3496) 2.35 4 -200.201 410.0 0.00 0.543
## m3 NB(2.3183) 2.32 4 -200.464 410.5 0.53 0.417
## m5 NB(1.988) 1.99 4 -202.964 415.5 5.53 0.034
## m6 -0.0512 NB(1.8048) 1.8 4 -204.735 419.1 9.07 0.006
## m4 NB(1.5004) 1.5 4 -207.966 425.5 15.53 0.000
## m2 NB(1.3551) 1.36 4 -209.783 429.2 19.16 0.000
## m0 NB(1.2186) 1.22 3 -211.709 430.3 20.34 0.000
## Abbreviations:
## family: NB(1.2186) = 'Negative Binomial(1.2186)',
## NB(1.3551) = 'Negative Binomial(1.3551)',
## NB(1.5004) = 'Negative Binomial(1.5004)',
## NB(1.8048) = 'Negative Binomial(1.8048)',
## NB(1.988) = 'Negative Binomial(1.988)',
## NB(2.3183) = 'Negative Binomial(2.3183)',
## NB(2.3496) = 'Negative Binomial(2.3496)'
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m1 3.895 -0.02447 0.57010
## m3 4.022 0.44860 -4.763
## m5 6.268 0.03512 -0.09511
## m6 1.790 0.82880
## m4 3.355 0.24820 0.0344
## m2 2.764 0.44130 3.681
## lst_wet df logLik AICc delta weight
## m1 3 -200.201 407.3 0.00 0.549
## m3 3 -200.466 407.9 0.53 0.421
## m5 3 -203.210 413.3 6.02 0.027
## m6 -0.05109 3 -205.367 417.7 10.33 0.003
## m4 3 -209.957 426.8 19.51 0.000
## m2 3 -212.937 432.8 25.47 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) cv.dpt prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m0 -4.508 1.689
## m6 -5.520 1.959
## m2 -3.606 1.434 4.69
## m5 -3.826 1.617 -0.04691
## m3 -4.005 1.640 -0.8627
## m1 -3.551 1.547 -0.001422
## m4 -4.297 1.640 0.00216
## lst_wet family init.theta df logLik AICc delta weight
## m0 NB(0.6203) 0.62 3 -184.783 376.5 0.00 0.266
## m6 -0.02405 NB(0.6581) 0.658 4 -183.706 377.0 0.52 0.205
## m2 NB(0.6471) 0.647 4 -184.011 377.6 1.13 0.151
## m5 NB(0.6389) 0.639 4 -184.244 378.1 1.60 0.120
## m3 NB(0.6328) 0.633 4 -184.417 378.4 1.94 0.101
## m1 NB(0.6277) 0.628 4 -184.566 378.7 2.24 0.087
## m4 NB(0.6206) 0.621 4 -184.774 379.1 2.66 0.070
## Abbreviations:
## family: NB(0.6203) = 'Negative Binomial(0.6203)',
## NB(0.6206) = 'Negative Binomial(0.6206)',
## NB(0.6277) = 'Negative Binomial(0.6277)',
## NB(0.6328) = 'Negative Binomial(0.6328)',
## NB(0.6389) = 'Negative Binomial(0.6389)',
## NB(0.6471) = 'Negative Binomial(0.6471)',
## NB(0.6581) = 'Negative Binomial(0.6581)'
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m6 -5.519 1.959
## m2 -3.605 1.434 4.69
## m5 -3.826 1.617 -0.04691
## m3 -4.006 1.641 -0.8628
## m1 -3.551 -0.001422 1.547
## m4 -4.296 1.640 0.002161
## lst_wet df logLik AICc delta weight
## m6 -0.02404 3 -183.738 374.4 0.00 0.275
## m2 3 -184.028 375.0 0.58 0.205
## m5 3 -184.252 375.4 1.03 0.164
## m3 3 -184.421 375.8 1.37 0.139
## m1 3 -184.567 376.1 1.66 0.120
## m4 3 -184.774 376.5 2.07 0.097
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) cv.dpt prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m0 -4.508 1.689
## m6 -5.520 1.959
## m2 -3.606 1.434 4.69
## m5 -3.826 1.617 -0.04691
## m3 -4.005 1.640 -0.8627
## m1 -3.551 1.547 -0.001422
## m4 -4.297 1.640 0.00216
## lst_wet family init.theta df logLik AICc delta weight
## m0 NB(0.6203) 0.62 3 -184.783 376.5 0.00 0.266
## m6 -0.02405 NB(0.6581) 0.658 4 -183.706 377.0 0.52 0.205
## m2 NB(0.6471) 0.647 4 -184.011 377.6 1.13 0.151
## m5 NB(0.6389) 0.639 4 -184.244 378.1 1.60 0.120
## m3 NB(0.6328) 0.633 4 -184.417 378.4 1.94 0.101
## m1 NB(0.6277) 0.628 4 -184.566 378.7 2.24 0.087
## m4 NB(0.6206) 0.621 4 -184.774 379.1 2.66 0.070
## Abbreviations:
## family: NB(0.6203) = 'Negative Binomial(0.6203)',
## NB(0.6206) = 'Negative Binomial(0.6206)',
## NB(0.6277) = 'Negative Binomial(0.6277)',
## NB(0.6328) = 'Negative Binomial(0.6328)',
## NB(0.6389) = 'Negative Binomial(0.6389)',
## NB(0.6471) = 'Negative Binomial(0.6471)',
## NB(0.6581) = 'Negative Binomial(0.6581)'
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m6 -5.519 1.959
## m2 -3.605 1.434 4.69
## m5 -3.826 1.617 -0.04691
## m3 -4.006 1.641 -0.8628
## m1 -3.551 -0.001422 1.547
## m4 -4.296 1.640 0.002161
## lst_wet df logLik AICc delta weight
## m6 -0.02404 3 -183.738 374.4 0.00 0.275
## m2 3 -184.028 375.0 0.58 0.205
## m5 3 -184.252 375.4 1.03 0.164
## m3 3 -184.421 375.8 1.37 0.139
## m1 3 -184.567 376.1 1.66 0.120
## m4 3 -184.774 376.5 2.07 0.097
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) cv.dpt prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m0 -4.508 1.689
## m6 -5.520 1.959
## m2 -3.606 1.434 4.69
## m5 -3.826 1.617 -0.04691
## m3 -4.005 1.640 -0.8627
## m1 -3.551 1.547 -0.001422
## m4 -4.297 1.640 0.00216
## lst_wet family init.theta df logLik AICc delta weight
## m0 NB(0.6203) 0.62 3 -184.783 376.5 0.00 0.266
## m6 -0.02405 NB(0.6581) 0.658 4 -183.706 377.0 0.52 0.205
## m2 NB(0.6471) 0.647 4 -184.011 377.6 1.13 0.151
## m5 NB(0.6389) 0.639 4 -184.244 378.1 1.60 0.120
## m3 NB(0.6328) 0.633 4 -184.417 378.4 1.94 0.101
## m1 NB(0.6277) 0.628 4 -184.566 378.7 2.24 0.087
## m4 NB(0.6206) 0.621 4 -184.774 379.1 2.66 0.070
## Abbreviations:
## family: NB(0.6203) = 'Negative Binomial(0.6203)',
## NB(0.6206) = 'Negative Binomial(0.6206)',
## NB(0.6277) = 'Negative Binomial(0.6277)',
## NB(0.6328) = 'Negative Binomial(0.6328)',
## NB(0.6389) = 'Negative Binomial(0.6389)',
## NB(0.6471) = 'Negative Binomial(0.6471)',
## NB(0.6581) = 'Negative Binomial(0.6581)'
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m6 -5.519 1.959
## m2 -3.605 1.434 4.69
## m5 -3.826 1.617 -0.04691
## m3 -4.006 1.641 -0.8628
## m1 -3.551 -0.001422 1.547
## m4 -4.296 1.640 0.002161
## lst_wet df logLik AICc delta weight
## m6 -0.02404 3 -183.738 374.4 0.00 0.275
## m2 3 -184.028 375.0 0.58 0.205
## m5 3 -184.252 375.4 1.03 0.164
## m3 3 -184.421 375.8 1.37 0.139
## m1 3 -184.567 376.1 1.66 0.120
## m4 3 -184.774 376.5 2.07 0.097
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) cv.dpt prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m0 -4.508 1.689
## m6 -5.520 1.959
## m2 -3.606 1.434 4.69
## m5 -3.826 1.617 -0.04691
## m3 -4.005 1.640 -0.8627
## m1 -3.551 1.547 -0.001422
## m4 -4.297 1.640 0.00216
## lst_wet family init.theta df logLik AICc delta weight
## m0 NB(0.6203) 0.62 3 -184.783 376.5 0.00 0.266
## m6 -0.02405 NB(0.6581) 0.658 4 -183.706 377.0 0.52 0.205
## m2 NB(0.6471) 0.647 4 -184.011 377.6 1.13 0.151
## m5 NB(0.6389) 0.639 4 -184.244 378.1 1.60 0.120
## m3 NB(0.6328) 0.633 4 -184.417 378.4 1.94 0.101
## m1 NB(0.6277) 0.628 4 -184.566 378.7 2.24 0.087
## m4 NB(0.6206) 0.621 4 -184.774 379.1 2.66 0.070
## Abbreviations:
## family: NB(0.6203) = 'Negative Binomial(0.6203)',
## NB(0.6206) = 'Negative Binomial(0.6206)',
## NB(0.6277) = 'Negative Binomial(0.6277)',
## NB(0.6328) = 'Negative Binomial(0.6328)',
## NB(0.6389) = 'Negative Binomial(0.6389)',
## NB(0.6471) = 'Negative Binomial(0.6471)',
## NB(0.6581) = 'Negative Binomial(0.6581)'
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m6 -5.519 1.959
## m2 -3.605 1.434 4.69
## m5 -3.826 1.617 -0.04691
## m3 -4.006 1.641 -0.8628
## m1 -3.551 -0.001422 1.547
## m4 -4.296 1.640 0.002161
## lst_wet df logLik AICc delta weight
## m6 -0.02404 3 -183.738 374.4 0.00 0.275
## m2 3 -184.028 375.0 0.58 0.205
## m5 3 -184.252 375.4 1.03 0.164
## m3 3 -184.421 375.8 1.37 0.139
## m1 3 -184.567 376.1 1.66 0.120
## m4 3 -184.774 376.5 2.07 0.097
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) cv.dpt prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m6 17.77 -1.693
## m0 17.18 -1.651
## m4 17.83 -1.835 0.009407
## m1 17.06 -1.615 -0.003578
## m3 17.11 -1.637 -0.7095
## m5 17.14 -1.644 -0.00981
## m2 17.75 -1.744 0.9793
## lst_wet family init.theta df logLik AICc delta weight
## m6 -0.01075 NB(2.4766) 2.48 4 -197.397 404.4 0.00 0.319
## m0 NB(2.2352) 2.24 3 -199.129 405.2 0.79 0.215
## m4 NB(2.3807) 2.38 4 -198.060 405.7 1.33 0.164
## m1 NB(2.3107) 2.31 4 -198.566 406.7 2.34 0.099
## m3 NB(2.2814) 2.28 4 -198.782 407.2 2.77 0.080
## m5 NB(2.2505) 2.25 4 -199.014 407.6 3.23 0.063
## m2 NB(2.2406) 2.24 4 -199.088 407.8 3.38 0.059
## Abbreviations:
## family: NB(2.2352) = 'Negative Binomial(2.2352)',
## NB(2.2406) = 'Negative Binomial(2.2406)',
## NB(2.2505) = 'Negative Binomial(2.2505)',
## NB(2.2814) = 'Negative Binomial(2.2814)',
## NB(2.3107) = 'Negative Binomial(2.3107)',
## NB(2.3807) = 'Negative Binomial(2.3807)',
## NB(2.4766) = 'Negative Binomial(2.4766)'
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m6 17.77 -1.693
## m4 17.83 -1.835 0.009406
## m1 17.05 -0.003578 -1.614
## m3 17.11 -1.637 -0.7096
## m5 17.14 -1.643 -0.009813
## m2 17.75 -1.743 0.9783
## lst_wet df logLik AICc delta weight
## m6 -0.01075 3 -197.397 401.7 0.00 0.418
## m4 3 -198.074 403.1 1.35 0.212
## m1 3 -198.609 404.1 2.43 0.124
## m3 3 -198.842 404.6 2.89 0.099
## m5 3 -199.095 405.1 3.40 0.076
## m2 3 -199.177 405.3 3.56 0.070
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) cv.dpt prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m6 17.77 -1.693
## m0 17.18 -1.651
## m4 17.83 -1.835 0.009407
## m1 17.06 -1.615 -0.003578
## m3 17.11 -1.637 -0.7095
## m5 17.14 -1.644 -0.00981
## m2 17.75 -1.744 0.9793
## lst_wet family init.theta df logLik AICc delta weight
## m6 -0.01075 NB(2.4766) 2.48 4 -197.397 404.4 0.00 0.319
## m0 NB(2.2352) 2.24 3 -199.129 405.2 0.79 0.215
## m4 NB(2.3807) 2.38 4 -198.060 405.7 1.33 0.164
## m1 NB(2.3107) 2.31 4 -198.566 406.7 2.34 0.099
## m3 NB(2.2814) 2.28 4 -198.782 407.2 2.77 0.080
## m5 NB(2.2505) 2.25 4 -199.014 407.6 3.23 0.063
## m2 NB(2.2406) 2.24 4 -199.088 407.8 3.38 0.059
## Abbreviations:
## family: NB(2.2352) = 'Negative Binomial(2.2352)',
## NB(2.2406) = 'Negative Binomial(2.2406)',
## NB(2.2505) = 'Negative Binomial(2.2505)',
## NB(2.2814) = 'Negative Binomial(2.2814)',
## NB(2.3107) = 'Negative Binomial(2.3107)',
## NB(2.3807) = 'Negative Binomial(2.3807)',
## NB(2.4766) = 'Negative Binomial(2.4766)'
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m6 17.77 -1.693
## m4 17.83 -1.835 0.009406
## m1 17.05 -0.003578 -1.614
## m3 17.11 -1.637 -0.7096
## m5 17.14 -1.643 -0.009813
## m2 17.75 -1.743 0.9783
## lst_wet df logLik AICc delta weight
## m6 -0.01075 3 -197.397 401.7 0.00 0.418
## m4 3 -198.074 403.1 1.35 0.212
## m1 3 -198.609 404.1 2.43 0.124
## m3 3 -198.842 404.6 2.89 0.099
## m5 3 -199.095 405.1 3.40 0.076
## m2 3 -199.177 405.3 3.56 0.070
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) cv.dpt prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m6 17.77 -1.693
## m0 17.18 -1.651
## m4 17.83 -1.835 0.009407
## m1 17.06 -1.615 -0.003578
## m3 17.11 -1.637 -0.7095
## m5 17.14 -1.644 -0.00981
## m2 17.75 -1.744 0.9793
## lst_wet family init.theta df logLik AICc delta weight
## m6 -0.01075 NB(2.4766) 2.48 4 -197.397 404.4 0.00 0.319
## m0 NB(2.2352) 2.24 3 -199.129 405.2 0.79 0.215
## m4 NB(2.3807) 2.38 4 -198.060 405.7 1.33 0.164
## m1 NB(2.3107) 2.31 4 -198.566 406.7 2.34 0.099
## m3 NB(2.2814) 2.28 4 -198.782 407.2 2.77 0.080
## m5 NB(2.2505) 2.25 4 -199.014 407.6 3.23 0.063
## m2 NB(2.2406) 2.24 4 -199.088 407.8 3.38 0.059
## Abbreviations:
## family: NB(2.2352) = 'Negative Binomial(2.2352)',
## NB(2.2406) = 'Negative Binomial(2.2406)',
## NB(2.2505) = 'Negative Binomial(2.2505)',
## NB(2.2814) = 'Negative Binomial(2.2814)',
## NB(2.3107) = 'Negative Binomial(2.3107)',
## NB(2.3807) = 'Negative Binomial(2.3807)',
## NB(2.4766) = 'Negative Binomial(2.4766)'
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m6 17.77 -1.693
## m4 17.83 -1.835 0.009406
## m1 17.05 -0.003578 -1.614
## m3 17.11 -1.637 -0.7096
## m5 17.14 -1.643 -0.009813
## m2 17.75 -1.743 0.9783
## lst_wet df logLik AICc delta weight
## m6 -0.01075 3 -197.397 401.7 0.00 0.418
## m4 3 -198.074 403.1 1.35 0.212
## m1 3 -198.609 404.1 2.43 0.124
## m3 3 -198.842 404.6 2.89 0.099
## m5 3 -199.095 405.1 3.40 0.076
## m2 3 -199.177 405.3 3.56 0.070
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) cv.dpt prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m4 5.557 0.1038 0.01645
## m0 3.341 0.6655
## m3 4.012 0.5491 -2.188
## m5 3.920 0.5666 -0.04555
## m1 4.580 0.4671 -0.006584
## m6 3.828 0.5841
## m2 3.594 0.6052 0.2897
## lst_wet family init.theta df logLik AICc delta weight
## m4 NB(3.4633) 3.46 4 -228.748 467.1 0.00 0.291
## m0 NB(3.1657) 3.17 3 -230.221 467.4 0.27 0.254
## m3 NB(3.251) 3.25 4 -229.783 469.2 2.07 0.103
## m5 NB(3.2463) 3.25 4 -229.807 469.2 2.12 0.101
## m1 NB(3.2282) 3.23 4 -229.899 469.4 2.30 0.092
## m6 -0.01021 NB(3.2113) 3.21 4 -229.985 469.6 2.47 0.084
## m2 NB(3.1889) 3.19 4 -230.100 469.8 2.70 0.075
## Abbreviations:
## family: NB(3.1657) = 'Negative Binomial(3.1657)',
## NB(3.1889) = 'Negative Binomial(3.1889)',
## NB(3.2113) = 'Negative Binomial(3.2113)',
## NB(3.2282) = 'Negative Binomial(3.2282)',
## NB(3.2463) = 'Negative Binomial(3.2463)',
## NB(3.251) = 'Negative Binomial(3.251)',
## NB(3.4633) = 'Negative Binomial(3.4633)'
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m4 5.557 0.1038 0.01645
## m3 4.012 0.5491 -2.188
## m5 3.920 0.5666 -0.04555
## m1 4.580 -0.006584 0.4671
## m6 3.828 0.5841
## m2 3.594 0.6052 0.2897
## lst_wet df logLik AICc delta weight
## m4 3 -228.748 464.4 0.00 0.399
## m3 3 -229.815 466.6 2.13 0.137
## m5 3 -229.841 466.6 2.19 0.134
## m1 3 -229.939 466.8 2.38 0.121
## m6 -0.01021 3 -230.032 467.0 2.57 0.111
## m2 3 -230.156 467.2 2.82 0.098
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) cv.dpt prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m4 5.557 0.1038 0.01645
## m0 3.341 0.6655
## m3 4.012 0.5491 -2.188
## m5 3.920 0.5666 -0.04555
## m1 4.580 0.4671 -0.006584
## m6 3.828 0.5841
## m2 3.594 0.6052 0.2897
## lst_wet family init.theta df logLik AICc delta weight
## m4 NB(3.4633) 3.46 4 -228.748 467.1 0.00 0.291
## m0 NB(3.1657) 3.17 3 -230.221 467.4 0.27 0.254
## m3 NB(3.251) 3.25 4 -229.783 469.2 2.07 0.103
## m5 NB(3.2463) 3.25 4 -229.807 469.2 2.12 0.101
## m1 NB(3.2282) 3.23 4 -229.899 469.4 2.30 0.092
## m6 -0.01021 NB(3.2113) 3.21 4 -229.985 469.6 2.47 0.084
## m2 NB(3.1889) 3.19 4 -230.100 469.8 2.70 0.075
## Abbreviations:
## family: NB(3.1657) = 'Negative Binomial(3.1657)',
## NB(3.1889) = 'Negative Binomial(3.1889)',
## NB(3.2113) = 'Negative Binomial(3.2113)',
## NB(3.2282) = 'Negative Binomial(3.2282)',
## NB(3.2463) = 'Negative Binomial(3.2463)',
## NB(3.251) = 'Negative Binomial(3.251)',
## NB(3.4633) = 'Negative Binomial(3.4633)'
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m4 5.557 0.1038 0.01645
## m3 4.012 0.5491 -2.188
## m5 3.920 0.5666 -0.04555
## m1 4.580 -0.006584 0.4671
## m6 3.828 0.5841
## m2 3.594 0.6052 0.2897
## lst_wet df logLik AICc delta weight
## m4 3 -228.748 464.4 0.00 0.399
## m3 3 -229.815 466.6 2.13 0.137
## m5 3 -229.841 466.6 2.19 0.134
## m1 3 -229.939 466.8 2.38 0.121
## m6 -0.01021 3 -230.032 467.0 2.57 0.111
## m2 3 -230.156 467.2 2.82 0.098
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) cv.dpt prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m0 -4.087 1.578
## m6 -4.183 1.630
## m2 -2.735 1.357 -1.111
## m4 -4.771 1.753 -0.01176
## m1 -4.732 1.664 0.003251
## m3 -4.522 1.653 0.2273
## m5 -4.292 1.614 0.01443
## lst_wet family init.theta df logLik AICc delta weight
## m0 NB(1.1672) 1.17 3 -151.610 310.1 0.00 0.352
## m6 -0.01178 NB(1.1988) 1.2 4 -151.169 311.9 1.79 0.143
## m2 NB(1.1794) 1.18 4 -151.447 312.5 2.35 0.109
## m4 NB(1.1778) 1.18 4 -151.466 312.5 2.39 0.107
## m1 NB(1.1744) 1.17 4 -151.515 312.6 2.49 0.101
## m3 NB(1.1698) 1.17 4 -151.576 312.8 2.61 0.095
## m5 NB(1.1677) 1.17 4 -151.604 312.8 2.66 0.093
## Abbreviations:
## family: NB(1.1672) = 'Negative Binomial(1.1672)',
## NB(1.1677) = 'Negative Binomial(1.1677)',
## NB(1.1698) = 'Negative Binomial(1.1698)',
## NB(1.1744) = 'Negative Binomial(1.1744)',
## NB(1.1778) = 'Negative Binomial(1.1778)',
## NB(1.1794) = 'Negative Binomial(1.1794)',
## NB(1.1988) = 'Negative Binomial(1.1988)'
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m6 -4.183 1.630
## m2 -2.734 1.357 -1.111
## m4 -4.770 1.753 -0.01176
## m1 -4.732 0.003251 1.664
## m3 -4.522 1.653 0.2273
## m5 -4.292 1.614 0.01443
## lst_wet df logLik AICc delta weight
## m6 -0.01178 3 -151.174 309.3 0.00 0.220
## m2 3 -151.448 309.8 0.55 0.168
## m4 3 -151.466 309.9 0.58 0.165
## m1 3 -151.515 310.0 0.68 0.157
## m3 3 -151.576 310.1 0.80 0.147
## m5 3 -151.604 310.1 0.86 0.143
## Models ranked by AICc(x)
## Error in nrow(concord.out): object 'concord.out' not found
sites<-c("puertorico","argentina", "macae", "frenchguiana", "costarica", "colombia")
concord.out3<-concord.magic(sites, "shredder_bio", nocadata, 100, 2, "nb")#works fine now save 2 models
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 4.814 0.3203
## m2 4.577 0.3567 0.4036
## m1 4.582 0.3619 0.04829
## m4 4.500 0.3941 0.4044 -0.4569
## m3 4.780 0.3307 0.02673 -0.01809
## m9 4.491 0.3734 0.08333 0.3785
## m10 4.993 0.3083 -0.02215 0.3705 -0.3876
## m11 6.000 0.1300 0.01105 0.1648 -0.09858
## m12 4.190 0.4190 0.16510 -0.1548 0.15910 0.8790
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m2
## m1
## m4
## m3
## m9 -0.2399
## m10 -0.2746 0.4539
## m11 -0.1124
## m12 -0.1452 -0.4141
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 family
## m0 NB(0.958)
## m2 NB(0.9966)
## m1 NB(0.9599)
## m4 NB(1.0059)
## m3 NB(0.9601)
## m9 NB(1.0125)
## m10 NB(1.0346)
## m11 0.2963 NB(1.033)
## m12 0.6313 -1.473 NB(1.2332)
## init.theta df logLik AICc delta weight
## m0 0.958 3 -230.574 468.1 0.00 0.489
## m2 0.997 4 -229.872 469.3 1.27 0.258
## m1 0.96 4 -230.539 470.7 2.61 0.133
## m4 1.01 5 -229.709 471.9 3.85 0.071
## m3 0.96 5 -230.533 473.6 5.50 0.031
## m9 1.01 6 -229.593 474.8 6.77 0.017
## m10 1.03 8 -229.215 481.3 13.22 0.001
## m11 1.03 8 -229.238 481.3 13.26 0.001
## m12 1.23 11 -226.026 488.7 20.65 0.000
## Abbreviations:
## family: NB(0.958) = 'Negative Binomial(0.958)',
## NB(0.9599) = 'Negative Binomial(0.9599)',
## NB(0.9601) = 'Negative Binomial(0.9601)',
## NB(0.9966) = 'Negative Binomial(0.9966)',
## NB(1.0059) = 'Negative Binomial(1.0059)',
## NB(1.0125) = 'Negative Binomial(1.0125)',
## NB(1.033) = 'Negative Binomial(1.033)',
## NB(1.0346) = 'Negative Binomial(1.0346)',
## NB(1.2332) = 'Negative Binomial(1.2332)'
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 4.814 0.3202
## m2 4.577 0.3567 0.4036
## m1 4.582 0.3619 0.04829
## m4 4.500 0.3941 0.4044 -0.4569
## m3 4.780 0.3307 0.02672 -0.01810
## m9 4.491 0.3734 0.08334 0.3785
## m10 4.994 0.3081 -0.02219 0.3705 -0.3875
## m11 6.000 0.1299 0.01104 0.1647 -0.09861
## m12 4.231 0.4123 0.16300 -0.1664 0.15790 0.8932
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m2
## m1
## m4
## m3
## m9 -0.2400
## m10 -0.2746 0.4541
## m11 -0.1124
## m12 -0.1440 -0.4112
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik
## m0 2 -230.574
## m2 3 -229.886
## m1 3 -230.539
## m4 4 -229.730
## m3 4 -230.534
## m9 5 -229.620
## m10 7 -229.266
## m11 0.2964 7 -229.287
## m12 0.6434 -1.487 10 -226.535
## AICc delta weight
## m0 465.6 0.00 0.458
## m2 466.7 1.10 0.264
## m1 468.0 2.41 0.137
## m4 469.1 3.47 0.081
## m3 470.7 5.08 0.036
## m9 471.7 6.15 0.021
## m10 477.6 12.03 0.001
## m11 477.7 12.07 0.001
## m12 484.6 19.06 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 4.814 0.3203
## m2 4.577 0.3567 0.4036
## m1 4.582 0.3619 0.04829
## m4 4.500 0.3941 0.4044 -0.4569
## m3 4.780 0.3307 0.02673 -0.01809
## m9 4.491 0.3734 0.08333 0.3785
## m10 4.993 0.3083 -0.02215 0.3705 -0.3876
## m11 6.000 0.1300 0.01105 0.1648 -0.09858
## m12 4.190 0.4190 0.16510 -0.1548 0.15910 0.8790
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m2
## m1
## m4
## m3
## m9 -0.2399
## m10 -0.2746 0.4539
## m11 -0.1124
## m12 -0.1452 -0.4141
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 family
## m0 NB(0.958)
## m2 NB(0.9966)
## m1 NB(0.9599)
## m4 NB(1.0059)
## m3 NB(0.9601)
## m9 NB(1.0125)
## m10 NB(1.0346)
## m11 0.2963 NB(1.033)
## m12 0.6313 -1.473 NB(1.2332)
## init.theta df logLik AICc delta weight
## m0 0.958 3 -230.574 468.1 0.00 0.489
## m2 0.997 4 -229.872 469.3 1.27 0.258
## m1 0.96 4 -230.539 470.7 2.61 0.133
## m4 1.01 5 -229.709 471.9 3.85 0.071
## m3 0.96 5 -230.533 473.6 5.50 0.031
## m9 1.01 6 -229.593 474.8 6.77 0.017
## m10 1.03 8 -229.215 481.3 13.22 0.001
## m11 1.03 8 -229.238 481.3 13.26 0.001
## m12 1.23 11 -226.026 488.7 20.65 0.000
## Abbreviations:
## family: NB(0.958) = 'Negative Binomial(0.958)',
## NB(0.9599) = 'Negative Binomial(0.9599)',
## NB(0.9601) = 'Negative Binomial(0.9601)',
## NB(0.9966) = 'Negative Binomial(0.9966)',
## NB(1.0059) = 'Negative Binomial(1.0059)',
## NB(1.0125) = 'Negative Binomial(1.0125)',
## NB(1.033) = 'Negative Binomial(1.033)',
## NB(1.0346) = 'Negative Binomial(1.0346)',
## NB(1.2332) = 'Negative Binomial(1.2332)'
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 4.814 0.3202
## m2 4.577 0.3567 0.4036
## m1 4.582 0.3619 0.04829
## m4 4.500 0.3941 0.4044 -0.4569
## m3 4.780 0.3307 0.02672 -0.01810
## m9 4.491 0.3734 0.08334 0.3785
## m10 4.994 0.3081 -0.02219 0.3705 -0.3875
## m11 6.000 0.1299 0.01104 0.1647 -0.09861
## m12 4.231 0.4123 0.16300 -0.1664 0.15790 0.8932
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m2
## m1
## m4
## m3
## m9 -0.2400
## m10 -0.2746 0.4541
## m11 -0.1124
## m12 -0.1440 -0.4112
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik
## m0 2 -230.574
## m2 3 -229.886
## m1 3 -230.539
## m4 4 -229.730
## m3 4 -230.534
## m9 5 -229.620
## m10 7 -229.266
## m11 0.2964 7 -229.287
## m12 0.6434 -1.487 10 -226.535
## AICc delta weight
## m0 465.6 0.00 0.458
## m2 466.7 1.10 0.264
## m1 468.0 2.41 0.137
## m4 469.1 3.47 0.081
## m3 470.7 5.08 0.036
## m9 471.7 6.15 0.021
## m10 477.6 12.03 0.001
## m11 477.7 12.07 0.001
## m12 484.6 19.06 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 4.814 0.3203
## m2 4.577 0.3567 0.4036
## m1 4.582 0.3619 0.04829
## m4 4.500 0.3941 0.4044 -0.4569
## m3 4.780 0.3307 0.02673 -0.01809
## m9 4.491 0.3734 0.08333 0.3785
## m10 4.993 0.3083 -0.02215 0.3705 -0.3876
## m11 6.000 0.1300 0.01105 0.1648 -0.09858
## m12 4.190 0.4190 0.16510 -0.1548 0.15910 0.8790
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m2
## m1
## m4
## m3
## m9 -0.2399
## m10 -0.2746 0.4539
## m11 -0.1124
## m12 -0.1452 -0.4141
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 family
## m0 NB(0.958)
## m2 NB(0.9966)
## m1 NB(0.9599)
## m4 NB(1.0059)
## m3 NB(0.9601)
## m9 NB(1.0125)
## m10 NB(1.0346)
## m11 0.2963 NB(1.033)
## m12 0.6313 -1.473 NB(1.2332)
## init.theta df logLik AICc delta weight
## m0 0.958 3 -230.574 468.1 0.00 0.489
## m2 0.997 4 -229.872 469.3 1.27 0.258
## m1 0.96 4 -230.539 470.7 2.61 0.133
## m4 1.01 5 -229.709 471.9 3.85 0.071
## m3 0.96 5 -230.533 473.6 5.50 0.031
## m9 1.01 6 -229.593 474.8 6.77 0.017
## m10 1.03 8 -229.215 481.3 13.22 0.001
## m11 1.03 8 -229.238 481.3 13.26 0.001
## m12 1.23 11 -226.026 488.7 20.65 0.000
## Abbreviations:
## family: NB(0.958) = 'Negative Binomial(0.958)',
## NB(0.9599) = 'Negative Binomial(0.9599)',
## NB(0.9601) = 'Negative Binomial(0.9601)',
## NB(0.9966) = 'Negative Binomial(0.9966)',
## NB(1.0059) = 'Negative Binomial(1.0059)',
## NB(1.0125) = 'Negative Binomial(1.0125)',
## NB(1.033) = 'Negative Binomial(1.033)',
## NB(1.0346) = 'Negative Binomial(1.0346)',
## NB(1.2332) = 'Negative Binomial(1.2332)'
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 4.814 0.3202
## m2 4.577 0.3567 0.4036
## m1 4.582 0.3619 0.04829
## m4 4.500 0.3941 0.4044 -0.4569
## m3 4.780 0.3307 0.02672 -0.01810
## m9 4.491 0.3734 0.08334 0.3785
## m10 4.994 0.3081 -0.02219 0.3705 -0.3875
## m11 6.000 0.1299 0.01104 0.1647 -0.09861
## m12 4.231 0.4123 0.16300 -0.1664 0.15790 0.8932
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m2
## m1
## m4
## m3
## m9 -0.2400
## m10 -0.2746 0.4541
## m11 -0.1124
## m12 -0.1440 -0.4112
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik
## m0 2 -230.574
## m2 3 -229.886
## m1 3 -230.539
## m4 4 -229.730
## m3 4 -230.534
## m9 5 -229.620
## m10 7 -229.266
## m11 0.2964 7 -229.287
## m12 0.6434 -1.487 10 -226.535
## AICc delta weight
## m0 465.6 0.00 0.458
## m2 466.7 1.10 0.264
## m1 468.0 2.41 0.137
## m4 469.1 3.47 0.081
## m3 470.7 5.08 0.036
## m9 471.7 6.15 0.021
## m10 477.6 12.03 0.001
## m11 477.7 12.07 0.001
## m12 484.6 19.06 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 4.814 0.3203
## m2 4.577 0.3567 0.4036
## m1 4.582 0.3619 0.04829
## m4 4.500 0.3941 0.4044 -0.4569
## m3 4.780 0.3307 0.02673 -0.01809
## m9 4.491 0.3734 0.08333 0.3785
## m10 4.993 0.3083 -0.02215 0.3705 -0.3876
## m11 6.000 0.1300 0.01105 0.1648 -0.09858
## m12 4.190 0.4190 0.16510 -0.1548 0.15910 0.8790
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m2
## m1
## m4
## m3
## m9 -0.2399
## m10 -0.2746 0.4539
## m11 -0.1124
## m12 -0.1452 -0.4141
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 family
## m0 NB(0.958)
## m2 NB(0.9966)
## m1 NB(0.9599)
## m4 NB(1.0059)
## m3 NB(0.9601)
## m9 NB(1.0125)
## m10 NB(1.0346)
## m11 0.2963 NB(1.033)
## m12 0.6313 -1.473 NB(1.2332)
## init.theta df logLik AICc delta weight
## m0 0.958 3 -230.574 468.1 0.00 0.489
## m2 0.997 4 -229.872 469.3 1.27 0.258
## m1 0.96 4 -230.539 470.7 2.61 0.133
## m4 1.01 5 -229.709 471.9 3.85 0.071
## m3 0.96 5 -230.533 473.6 5.50 0.031
## m9 1.01 6 -229.593 474.8 6.77 0.017
## m10 1.03 8 -229.215 481.3 13.22 0.001
## m11 1.03 8 -229.238 481.3 13.26 0.001
## m12 1.23 11 -226.026 488.7 20.65 0.000
## Abbreviations:
## family: NB(0.958) = 'Negative Binomial(0.958)',
## NB(0.9599) = 'Negative Binomial(0.9599)',
## NB(0.9601) = 'Negative Binomial(0.9601)',
## NB(0.9966) = 'Negative Binomial(0.9966)',
## NB(1.0059) = 'Negative Binomial(1.0059)',
## NB(1.0125) = 'Negative Binomial(1.0125)',
## NB(1.033) = 'Negative Binomial(1.033)',
## NB(1.0346) = 'Negative Binomial(1.0346)',
## NB(1.2332) = 'Negative Binomial(1.2332)'
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 4.814 0.3202
## m2 4.577 0.3567 0.4036
## m1 4.582 0.3619 0.04829
## m4 4.500 0.3941 0.4044 -0.4569
## m3 4.780 0.3307 0.02672 -0.01810
## m9 4.491 0.3734 0.08334 0.3785
## m10 4.994 0.3081 -0.02219 0.3705 -0.3875
## m11 6.000 0.1299 0.01104 0.1647 -0.09861
## m12 4.231 0.4123 0.16300 -0.1664 0.15790 0.8932
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m2
## m1
## m4
## m3
## m9 -0.2400
## m10 -0.2746 0.4541
## m11 -0.1124
## m12 -0.1440 -0.4112
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik
## m0 2 -230.574
## m2 3 -229.886
## m1 3 -230.539
## m4 4 -229.730
## m3 4 -230.534
## m9 5 -229.620
## m10 7 -229.266
## m11 0.2964 7 -229.287
## m12 0.6434 -1.487 10 -226.535
## AICc delta weight
## m0 465.6 0.00 0.458
## m2 466.7 1.10 0.264
## m1 468.0 2.41 0.137
## m4 469.1 3.47 0.081
## m3 470.7 5.08 0.036
## m9 471.7 6.15 0.021
## m10 477.6 12.03 0.001
## m11 477.7 12.07 0.001
## m12 484.6 19.06 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 4.814 0.3203
## m2 4.577 0.3567 0.4036
## m1 4.582 0.3619 0.04829
## m4 4.500 0.3941 0.4044 -0.4569
## m3 4.780 0.3307 0.02673 -0.01809
## m9 4.491 0.3734 0.08333 0.3785
## m10 4.993 0.3083 -0.02215 0.3705 -0.3876
## m11 6.000 0.1300 0.01105 0.1648 -0.09858
## m12 4.190 0.4190 0.16510 -0.1548 0.15910 0.8790
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m2
## m1
## m4
## m3
## m9 -0.2399
## m10 -0.2746 0.4539
## m11 -0.1124
## m12 -0.1452 -0.4141
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 family
## m0 NB(0.958)
## m2 NB(0.9966)
## m1 NB(0.9599)
## m4 NB(1.0059)
## m3 NB(0.9601)
## m9 NB(1.0125)
## m10 NB(1.0346)
## m11 0.2963 NB(1.033)
## m12 0.6313 -1.473 NB(1.2332)
## init.theta df logLik AICc delta weight
## m0 0.958 3 -230.574 468.1 0.00 0.489
## m2 0.997 4 -229.872 469.3 1.27 0.258
## m1 0.96 4 -230.539 470.7 2.61 0.133
## m4 1.01 5 -229.709 471.9 3.85 0.071
## m3 0.96 5 -230.533 473.6 5.50 0.031
## m9 1.01 6 -229.593 474.8 6.77 0.017
## m10 1.03 8 -229.215 481.3 13.22 0.001
## m11 1.03 8 -229.238 481.3 13.26 0.001
## m12 1.23 11 -226.026 488.7 20.65 0.000
## Abbreviations:
## family: NB(0.958) = 'Negative Binomial(0.958)',
## NB(0.9599) = 'Negative Binomial(0.9599)',
## NB(0.9601) = 'Negative Binomial(0.9601)',
## NB(0.9966) = 'Negative Binomial(0.9966)',
## NB(1.0059) = 'Negative Binomial(1.0059)',
## NB(1.0125) = 'Negative Binomial(1.0125)',
## NB(1.033) = 'Negative Binomial(1.033)',
## NB(1.0346) = 'Negative Binomial(1.0346)',
## NB(1.2332) = 'Negative Binomial(1.2332)'
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 4.814 0.3202
## m2 4.577 0.3567 0.4036
## m1 4.582 0.3619 0.04829
## m4 4.500 0.3941 0.4044 -0.4569
## m3 4.780 0.3307 0.02672 -0.01810
## m9 4.491 0.3734 0.08334 0.3785
## m10 4.994 0.3081 -0.02219 0.3705 -0.3875
## m11 6.000 0.1299 0.01104 0.1647 -0.09861
## m12 4.231 0.4123 0.16300 -0.1664 0.15790 0.8932
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m2
## m1
## m4
## m3
## m9 -0.2400
## m10 -0.2746 0.4541
## m11 -0.1124
## m12 -0.1440 -0.4112
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik
## m0 2 -230.574
## m2 3 -229.886
## m1 3 -230.539
## m4 4 -229.730
## m3 4 -230.534
## m9 5 -229.620
## m10 7 -229.266
## m11 0.2964 7 -229.287
## m12 0.6434 -1.487 10 -226.535
## AICc delta weight
## m0 465.6 0.00 0.458
## m2 466.7 1.10 0.264
## m1 468.0 2.41 0.137
## m4 469.1 3.47 0.081
## m3 470.7 5.08 0.036
## m9 471.7 6.15 0.021
## m10 477.6 12.03 0.001
## m11 477.7 12.07 0.001
## m12 484.6 19.06 0.000
## Models ranked by AICc(x)
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in glm.nb(y ~ log(maxvol) + log(mu.scalar) + I(log(mu.scalar)^2), :
## alternation limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in glm.nb(y ~ log(maxvol) + log(k.scalar) + I(log(k.scalar)^2), :
## alternation limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in glm.nb(y ~ log(maxvol) + log(mu.scalar) * log(k.scalar), data =
## dataset): alternation limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in glm.nb(y ~ log(maxvol) + log(mu.scalar) * (log(k.scalar) +
## I(log(k.scalar)^2)), : alternation limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in glm.nb(y ~ log(maxvol) + log(k.scalar) * (log(mu.scalar) +
## I(log(mu.scalar)^2)), : alternation limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in glm.nb(y ~ log(maxvol) + (log(k.scalar) + I(log(k.scalar)^2))
## * : alternation limit reached
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m12 9.099 -0.7472 -0.0412 0.08509 0.1340 -1.682
## m3 8.615 -0.7431 0.1392 0.2716
## m11 8.694 -0.7573 0.1387 0.03857 0.2725
## m10 9.064 -0.7185 -0.1969 0.07180 -1.078
## m4 7.873 -0.5007 0.02147 -1.058
## m9 8.688 -0.7070 -0.1885 0.05323
## m1 8.872 -0.7391 -0.1927
## m0 7.682 -0.5211
## m2 7.674 -0.5196 0.01500
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m12 0.06992 0.67450
## m3
## m11 0.07897
## m10 0.10520 0.01408
## m4
## m9 0.07644
## m1
## m0
## m2
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 family
## m12 0.01027 0.5211 NB(761653.9)
## m3 NB(831585.9)
## m11 0.02882 NB(854066.7)
## m10 NB(837032.7)
## m4 NB(951294.3)
## m9 NB(806391.1)
## m1 NB(802692.5)
## m0 NB(846723.8)
## m2 NB(847094.8)
## init.theta df logLik AICc delta weight
## m12 762000 11 -1629.502 3295.7 0.00 1
## m3 832000 5 -1769.218 3550.9 255.26 0
## m11 854000 8 -1765.676 3554.2 258.54 0
## m10 837000 8 -1807.770 3638.4 342.73 0
## m4 951000 5 -1880.958 3774.4 478.74 0
## m9 806000 6 -1913.781 3843.2 547.54 0
## m1 803000 4 -1917.546 3844.7 549.02 0
## m0 847000 3 -1982.630 3972.2 676.51 0
## m2 847000 4 -1982.513 3974.6 678.96 0
## Abbreviations:
## family: NB(761653.9) = 'Negative Binomial(761653.9)',
## NB(802692.5) = 'Negative Binomial(802692.5)',
## NB(806391.1) = 'Negative Binomial(806391.1)',
## NB(831585.9) = 'Negative Binomial(831585.9)',
## NB(837032.7) = 'Negative Binomial(837032.7)',
## NB(846723.8) = 'Negative Binomial(846723.8)',
## NB(847094.8) = 'Negative Binomial(847094.8)',
## NB(854066.7) = 'Negative Binomial(854066.7)',
## NB(951294.3) = 'Negative Binomial(951294.3)'
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m12 9.099 -0.7472 -0.0412 0.08509 0.1340 -1.682
## m3 8.615 -0.7431 0.1392 0.2716
## m11 8.694 -0.7573 0.1387 0.03857 0.2725
## m10 9.064 -0.7185 -0.1969 0.07180 -1.078
## m4 7.873 -0.5007 0.02147 -1.058
## m9 8.688 -0.7070 -0.1885 0.05323
## m1 8.872 -0.7391 -0.1927
## m0 7.682 -0.5211
## m2 7.674 -0.5196 0.01500
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m12 0.06992 0.67450
## m3
## m11 0.07897
## m10 0.10520 0.01408
## m4
## m9 0.07644
## m1
## m0
## m2
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik
## m12 0.01027 0.5211 10 -1629.502
## m3 4 -1769.199
## m11 0.02882 7 -1765.652
## m10 7 -1807.750
## m4 4 -1880.910
## m9 5 -1913.767
## m1 3 -1917.533
## m0 2 -1982.604
## m2 3 -1982.487
## AICc delta weight
## m12 3290.6 0.00 1
## m3 3548.0 257.41 0
## m11 3550.4 259.81 0
## m10 3634.6 344.01 0
## m4 3771.4 480.84 0
## m9 3840.0 549.45 0
## m1 3842.0 551.41 0
## m0 3969.7 679.07 0
## m2 3971.9 681.31 0
## Models ranked by AICc(x)
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in glm.nb(y ~ log(maxvol) + log(mu.scalar) + I(log(mu.scalar)^2), :
## alternation limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in glm.nb(y ~ log(maxvol) + log(k.scalar) + I(log(k.scalar)^2), :
## alternation limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in glm.nb(y ~ log(maxvol) + log(mu.scalar) * log(k.scalar), data =
## dataset): alternation limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in glm.nb(y ~ log(maxvol) + log(mu.scalar) * (log(k.scalar) +
## I(log(k.scalar)^2)), : alternation limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in glm.nb(y ~ log(maxvol) + log(k.scalar) * (log(mu.scalar) +
## I(log(mu.scalar)^2)), : alternation limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in glm.nb(y ~ log(maxvol) + (log(k.scalar) + I(log(k.scalar)^2))
## * : alternation limit reached
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m12 9.099 -0.7472 -0.0412 0.08509 0.1340 -1.682
## m3 8.615 -0.7431 0.1392 0.2716
## m11 8.694 -0.7573 0.1387 0.03857 0.2725
## m10 9.064 -0.7185 -0.1969 0.07180 -1.078
## m4 7.873 -0.5007 0.02147 -1.058
## m9 8.688 -0.7070 -0.1885 0.05323
## m1 8.872 -0.7391 -0.1927
## m0 7.682 -0.5211
## m2 7.674 -0.5196 0.01500
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m12 0.06992 0.67450
## m3
## m11 0.07897
## m10 0.10520 0.01408
## m4
## m9 0.07644
## m1
## m0
## m2
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 family
## m12 0.01027 0.5211 NB(761653.9)
## m3 NB(831585.9)
## m11 0.02882 NB(854066.7)
## m10 NB(837032.7)
## m4 NB(951294.3)
## m9 NB(806391.1)
## m1 NB(802692.5)
## m0 NB(846723.8)
## m2 NB(847094.8)
## init.theta df logLik AICc delta weight
## m12 762000 11 -1629.502 3295.7 0.00 1
## m3 832000 5 -1769.218 3550.9 255.26 0
## m11 854000 8 -1765.676 3554.2 258.54 0
## m10 837000 8 -1807.770 3638.4 342.73 0
## m4 951000 5 -1880.958 3774.4 478.74 0
## m9 806000 6 -1913.781 3843.2 547.54 0
## m1 803000 4 -1917.546 3844.7 549.02 0
## m0 847000 3 -1982.630 3972.2 676.51 0
## m2 847000 4 -1982.513 3974.6 678.96 0
## Abbreviations:
## family: NB(761653.9) = 'Negative Binomial(761653.9)',
## NB(802692.5) = 'Negative Binomial(802692.5)',
## NB(806391.1) = 'Negative Binomial(806391.1)',
## NB(831585.9) = 'Negative Binomial(831585.9)',
## NB(837032.7) = 'Negative Binomial(837032.7)',
## NB(846723.8) = 'Negative Binomial(846723.8)',
## NB(847094.8) = 'Negative Binomial(847094.8)',
## NB(854066.7) = 'Negative Binomial(854066.7)',
## NB(951294.3) = 'Negative Binomial(951294.3)'
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m12 9.099 -0.7472 -0.0412 0.08509 0.1340 -1.682
## m3 8.615 -0.7431 0.1392 0.2716
## m11 8.694 -0.7573 0.1387 0.03857 0.2725
## m10 9.064 -0.7185 -0.1969 0.07180 -1.078
## m4 7.873 -0.5007 0.02147 -1.058
## m9 8.688 -0.7070 -0.1885 0.05323
## m1 8.872 -0.7391 -0.1927
## m0 7.682 -0.5211
## m2 7.674 -0.5196 0.01500
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m12 0.06992 0.67450
## m3
## m11 0.07897
## m10 0.10520 0.01408
## m4
## m9 0.07644
## m1
## m0
## m2
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik
## m12 0.01027 0.5211 10 -1629.502
## m3 4 -1769.199
## m11 0.02882 7 -1765.652
## m10 7 -1807.750
## m4 4 -1880.910
## m9 5 -1913.767
## m1 3 -1917.533
## m0 2 -1982.604
## m2 3 -1982.487
## AICc delta weight
## m12 3290.6 0.00 1
## m3 3548.0 257.41 0
## m11 3550.4 259.81 0
## m10 3634.6 344.01 0
## m4 3771.4 480.84 0
## m9 3840.0 549.45 0
## m1 3842.0 551.41 0
## m0 3969.7 679.07 0
## m2 3971.9 681.31 0
## Models ranked by AICc(x)
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
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## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
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## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in glm.nb(y ~ log(maxvol) + log(mu.scalar) + I(log(mu.scalar)^2), :
## alternation limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
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## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in glm.nb(y ~ log(maxvol) + log(k.scalar) + I(log(k.scalar)^2), :
## alternation limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
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## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in glm.nb(y ~ log(maxvol) + log(mu.scalar) * log(k.scalar), data =
## dataset): alternation limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in glm.nb(y ~ log(maxvol) + log(mu.scalar) * (log(k.scalar) +
## I(log(k.scalar)^2)), : alternation limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in glm.nb(y ~ log(maxvol) + log(k.scalar) * (log(mu.scalar) +
## I(log(mu.scalar)^2)), : alternation limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in glm.nb(y ~ log(maxvol) + (log(k.scalar) + I(log(k.scalar)^2))
## * : alternation limit reached
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m12 9.099 -0.7472 -0.0412 0.08509 0.1340 -1.682
## m3 8.615 -0.7431 0.1392 0.2716
## m11 8.694 -0.7573 0.1387 0.03857 0.2725
## m10 9.064 -0.7185 -0.1969 0.07180 -1.078
## m4 7.873 -0.5007 0.02147 -1.058
## m9 8.688 -0.7070 -0.1885 0.05323
## m1 8.872 -0.7391 -0.1927
## m0 7.682 -0.5211
## m2 7.674 -0.5196 0.01500
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m12 0.06992 0.67450
## m3
## m11 0.07897
## m10 0.10520 0.01408
## m4
## m9 0.07644
## m1
## m0
## m2
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 family
## m12 0.01027 0.5211 NB(761653.9)
## m3 NB(831585.9)
## m11 0.02882 NB(854066.7)
## m10 NB(837032.7)
## m4 NB(951294.3)
## m9 NB(806391.1)
## m1 NB(802692.5)
## m0 NB(846723.8)
## m2 NB(847094.8)
## init.theta df logLik AICc delta weight
## m12 762000 11 -1629.502 3295.7 0.00 1
## m3 832000 5 -1769.218 3550.9 255.26 0
## m11 854000 8 -1765.676 3554.2 258.54 0
## m10 837000 8 -1807.770 3638.4 342.73 0
## m4 951000 5 -1880.958 3774.4 478.74 0
## m9 806000 6 -1913.781 3843.2 547.54 0
## m1 803000 4 -1917.546 3844.7 549.02 0
## m0 847000 3 -1982.630 3972.2 676.51 0
## m2 847000 4 -1982.513 3974.6 678.96 0
## Abbreviations:
## family: NB(761653.9) = 'Negative Binomial(761653.9)',
## NB(802692.5) = 'Negative Binomial(802692.5)',
## NB(806391.1) = 'Negative Binomial(806391.1)',
## NB(831585.9) = 'Negative Binomial(831585.9)',
## NB(837032.7) = 'Negative Binomial(837032.7)',
## NB(846723.8) = 'Negative Binomial(846723.8)',
## NB(847094.8) = 'Negative Binomial(847094.8)',
## NB(854066.7) = 'Negative Binomial(854066.7)',
## NB(951294.3) = 'Negative Binomial(951294.3)'
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m12 9.099 -0.7472 -0.0412 0.08509 0.1340 -1.682
## m3 8.615 -0.7431 0.1392 0.2716
## m11 8.694 -0.7573 0.1387 0.03857 0.2725
## m10 9.064 -0.7185 -0.1969 0.07180 -1.078
## m4 7.873 -0.5007 0.02147 -1.058
## m9 8.688 -0.7070 -0.1885 0.05323
## m1 8.872 -0.7391 -0.1927
## m0 7.682 -0.5211
## m2 7.674 -0.5196 0.01500
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m12 0.06992 0.67450
## m3
## m11 0.07897
## m10 0.10520 0.01408
## m4
## m9 0.07644
## m1
## m0
## m2
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik
## m12 0.01027 0.5211 10 -1629.502
## m3 4 -1769.199
## m11 0.02882 7 -1765.652
## m10 7 -1807.750
## m4 4 -1880.910
## m9 5 -1913.767
## m1 3 -1917.533
## m0 2 -1982.604
## m2 3 -1982.487
## AICc delta weight
## m12 3290.6 0.00 1
## m3 3548.0 257.41 0
## m11 3550.4 259.81 0
## m10 3634.6 344.01 0
## m4 3771.4 480.84 0
## m9 3840.0 549.45 0
## m1 3842.0 551.41 0
## m0 3969.7 679.07 0
## m2 3971.9 681.31 0
## Models ranked by AICc(x)
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
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## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in glm.nb(y ~ log(maxvol) + log(mu.scalar) + I(log(mu.scalar)^2), :
## alternation limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in glm.nb(y ~ log(maxvol) + log(k.scalar) + I(log(k.scalar)^2), :
## alternation limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in glm.nb(y ~ log(maxvol) + log(mu.scalar) * log(k.scalar), data =
## dataset): alternation limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in glm.nb(y ~ log(maxvol) + log(mu.scalar) * (log(k.scalar) +
## I(log(k.scalar)^2)), : alternation limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in glm.nb(y ~ log(maxvol) + log(k.scalar) * (log(mu.scalar) +
## I(log(mu.scalar)^2)), : alternation limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in glm.nb(y ~ log(maxvol) + (log(k.scalar) + I(log(k.scalar)^2))
## * : alternation limit reached
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m12 9.099 -0.7472 -0.0412 0.08509 0.1340 -1.682
## m3 8.615 -0.7431 0.1392 0.2716
## m11 8.694 -0.7573 0.1387 0.03857 0.2725
## m10 9.064 -0.7185 -0.1969 0.07180 -1.078
## m4 7.873 -0.5007 0.02147 -1.058
## m9 8.688 -0.7070 -0.1885 0.05323
## m1 8.872 -0.7391 -0.1927
## m0 7.682 -0.5211
## m2 7.674 -0.5196 0.01500
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m12 0.06992 0.67450
## m3
## m11 0.07897
## m10 0.10520 0.01408
## m4
## m9 0.07644
## m1
## m0
## m2
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 family
## m12 0.01027 0.5211 NB(761653.9)
## m3 NB(831585.9)
## m11 0.02882 NB(854066.7)
## m10 NB(837032.7)
## m4 NB(951294.3)
## m9 NB(806391.1)
## m1 NB(802692.5)
## m0 NB(846723.8)
## m2 NB(847094.8)
## init.theta df logLik AICc delta weight
## m12 762000 11 -1629.502 3295.7 0.00 1
## m3 832000 5 -1769.218 3550.9 255.26 0
## m11 854000 8 -1765.676 3554.2 258.54 0
## m10 837000 8 -1807.770 3638.4 342.73 0
## m4 951000 5 -1880.958 3774.4 478.74 0
## m9 806000 6 -1913.781 3843.2 547.54 0
## m1 803000 4 -1917.546 3844.7 549.02 0
## m0 847000 3 -1982.630 3972.2 676.51 0
## m2 847000 4 -1982.513 3974.6 678.96 0
## Abbreviations:
## family: NB(761653.9) = 'Negative Binomial(761653.9)',
## NB(802692.5) = 'Negative Binomial(802692.5)',
## NB(806391.1) = 'Negative Binomial(806391.1)',
## NB(831585.9) = 'Negative Binomial(831585.9)',
## NB(837032.7) = 'Negative Binomial(837032.7)',
## NB(846723.8) = 'Negative Binomial(846723.8)',
## NB(847094.8) = 'Negative Binomial(847094.8)',
## NB(854066.7) = 'Negative Binomial(854066.7)',
## NB(951294.3) = 'Negative Binomial(951294.3)'
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m12 9.099 -0.7472 -0.0412 0.08509 0.1340 -1.682
## m3 8.615 -0.7431 0.1392 0.2716
## m11 8.694 -0.7573 0.1387 0.03857 0.2725
## m10 9.064 -0.7185 -0.1969 0.07180 -1.078
## m4 7.873 -0.5007 0.02147 -1.058
## m9 8.688 -0.7070 -0.1885 0.05323
## m1 8.872 -0.7391 -0.1927
## m0 7.682 -0.5211
## m2 7.674 -0.5196 0.01500
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m12 0.06992 0.67450
## m3
## m11 0.07897
## m10 0.10520 0.01408
## m4
## m9 0.07644
## m1
## m0
## m2
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik
## m12 0.01027 0.5211 10 -1629.502
## m3 4 -1769.199
## m11 0.02882 7 -1765.652
## m10 7 -1807.750
## m4 4 -1880.910
## m9 5 -1913.767
## m1 3 -1917.533
## m0 2 -1982.604
## m2 3 -1982.487
## AICc delta weight
## m12 3290.6 0.00 1
## m3 3548.0 257.41 0
## m11 3550.4 259.81 0
## m10 3634.6 344.01 0
## m4 3771.4 480.84 0
## m9 3840.0 549.45 0
## m1 3842.0 551.41 0
## m0 3969.7 679.07 0
## m2 3971.9 681.31 0
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m3 -4.168 1.456 -0.4422 -0.3343
## m0 -5.300 1.589
## m1 -7.332 1.876 -0.1574
## m2 -4.595 1.486 0.094350
## m4 -4.797 1.514 0.092920 0.03181
## m9 -6.939 1.819 -0.1514 0.047920
## m11 -3.329 1.335 -0.4459 0.005123 -0.3419
## m10 -8.653 2.051 -0.3044 0.033900 0.33180
## m12 -3.240 1.319 -0.8179 0.017940 -0.4382 0.18500
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m3
## m0
## m1
## m2
## m4
## m9 -0.09333
## m11 0.02612
## m10 -0.10120 0.4450
## m12 0.02139 0.9812
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 family
## m3 NB(1.8576)
## m0 NB(1.5417)
## m1 NB(1.5809)
## m2 NB(1.5472)
## m4 NB(1.5473)
## m9 NB(1.5886)
## m11 0.10640 NB(1.8814)
## m10 NB(1.6094)
## m12 0.09483 0.1178 NB(2.008)
## init.theta df logLik AICc delta weight
## m3 1.86 5 -195.253 403.0 0.00 0.466
## m0 1.54 3 -198.487 403.9 0.89 0.299
## m1 1.58 4 -198.044 405.7 2.68 0.122
## m2 1.55 4 -198.424 406.4 3.44 0.083
## m4 1.55 5 -198.423 409.3 6.34 0.020
## m9 1.59 6 -197.959 411.6 8.56 0.006
## m11 1.88 8 -195.035 412.9 9.92 0.003
## m10 1.61 8 -197.730 418.3 15.31 0.000
## m12 2.01 11 -193.917 424.5 21.49 0.000
## Abbreviations:
## family: NB(1.5417) = 'Negative Binomial(1.5417)',
## NB(1.5472) = 'Negative Binomial(1.5472)',
## NB(1.5473) = 'Negative Binomial(1.5473)',
## NB(1.5809) = 'Negative Binomial(1.5809)',
## NB(1.5886) = 'Negative Binomial(1.5886)',
## NB(1.6094) = 'Negative Binomial(1.6094)',
## NB(1.8576) = 'Negative Binomial(1.8576)',
## NB(1.8814) = 'Negative Binomial(1.8814)',
## NB(2.008) = 'Negative Binomial(2.008)'
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m3 -4.168 1.456 -0.4422 -0.3343
## m0 -5.301 1.589
## m1 -7.332 1.876 -0.1573
## m2 -4.596 1.486 0.094340
## m4 -4.798 1.514 0.092910 0.0317
## m9 -6.939 1.819 -0.1514 0.047930
## m11 -3.329 1.335 -0.4459 0.005146 -0.3419
## m10 -8.651 2.051 -0.3042 0.033910 0.3315
## m12 -3.240 1.319 -0.8181 0.017890 -0.4383 0.1850
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m3
## m0
## m1
## m2
## m4
## m9 -0.09331
## m11 0.02612
## m10 -0.10120 0.4447
## m12 0.02145 0.9817
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik
## m3 4 -195.254
## m0 2 -198.812
## m1 3 -198.285
## m2 3 -198.737
## m4 4 -198.735
## m9 5 -198.186
## m11 0.10640 7 -195.036
## m10 7 -197.919
## m12 0.09489 0.1179 10 -193.965
## AICc delta weight
## m3 400.1 0.00 0.578
## m0 402.1 1.96 0.217
## m1 403.5 3.39 0.106
## m2 404.4 4.29 0.068
## m4 407.1 6.96 0.018
## m9 408.9 8.76 0.007
## m11 409.2 9.06 0.006
## m10 414.9 14.82 0.000
## m12 419.5 19.40 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m3 -4.168 1.456 -0.4422 -0.3343
## m0 -5.300 1.589
## m1 -7.332 1.876 -0.1574
## m2 -4.595 1.486 0.094350
## m4 -4.797 1.514 0.092920 0.03181
## m9 -6.939 1.819 -0.1514 0.047920
## m11 -3.329 1.335 -0.4459 0.005123 -0.3419
## m10 -8.653 2.051 -0.3044 0.033900 0.33180
## m12 -3.240 1.319 -0.8179 0.017940 -0.4382 0.18500
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m3
## m0
## m1
## m2
## m4
## m9 -0.09333
## m11 0.02612
## m10 -0.10120 0.4450
## m12 0.02139 0.9812
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 family
## m3 NB(1.8576)
## m0 NB(1.5417)
## m1 NB(1.5809)
## m2 NB(1.5472)
## m4 NB(1.5473)
## m9 NB(1.5886)
## m11 0.10640 NB(1.8814)
## m10 NB(1.6094)
## m12 0.09483 0.1178 NB(2.008)
## init.theta df logLik AICc delta weight
## m3 1.86 5 -195.253 403.0 0.00 0.466
## m0 1.54 3 -198.487 403.9 0.89 0.299
## m1 1.58 4 -198.044 405.7 2.68 0.122
## m2 1.55 4 -198.424 406.4 3.44 0.083
## m4 1.55 5 -198.423 409.3 6.34 0.020
## m9 1.59 6 -197.959 411.6 8.56 0.006
## m11 1.88 8 -195.035 412.9 9.92 0.003
## m10 1.61 8 -197.730 418.3 15.31 0.000
## m12 2.01 11 -193.917 424.5 21.49 0.000
## Abbreviations:
## family: NB(1.5417) = 'Negative Binomial(1.5417)',
## NB(1.5472) = 'Negative Binomial(1.5472)',
## NB(1.5473) = 'Negative Binomial(1.5473)',
## NB(1.5809) = 'Negative Binomial(1.5809)',
## NB(1.5886) = 'Negative Binomial(1.5886)',
## NB(1.6094) = 'Negative Binomial(1.6094)',
## NB(1.8576) = 'Negative Binomial(1.8576)',
## NB(1.8814) = 'Negative Binomial(1.8814)',
## NB(2.008) = 'Negative Binomial(2.008)'
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m3 -4.168 1.456 -0.4422 -0.3343
## m0 -5.301 1.589
## m1 -7.332 1.876 -0.1573
## m2 -4.596 1.486 0.094340
## m4 -4.798 1.514 0.092910 0.0317
## m9 -6.939 1.819 -0.1514 0.047930
## m11 -3.329 1.335 -0.4459 0.005146 -0.3419
## m10 -8.651 2.051 -0.3042 0.033910 0.3315
## m12 -3.240 1.319 -0.8181 0.017890 -0.4383 0.1850
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m3
## m0
## m1
## m2
## m4
## m9 -0.09331
## m11 0.02612
## m10 -0.10120 0.4447
## m12 0.02145 0.9817
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik
## m3 4 -195.254
## m0 2 -198.812
## m1 3 -198.285
## m2 3 -198.737
## m4 4 -198.735
## m9 5 -198.186
## m11 0.10640 7 -195.036
## m10 7 -197.919
## m12 0.09489 0.1179 10 -193.965
## AICc delta weight
## m3 400.1 0.00 0.578
## m0 402.1 1.96 0.217
## m1 403.5 3.39 0.106
## m2 404.4 4.29 0.068
## m4 407.1 6.96 0.018
## m9 408.9 8.76 0.007
## m11 409.2 9.06 0.006
## m10 414.9 14.82 0.000
## m12 419.5 19.40 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m3 -4.168 1.456 -0.4422 -0.3343
## m0 -5.300 1.589
## m1 -7.332 1.876 -0.1574
## m2 -4.595 1.486 0.094350
## m4 -4.797 1.514 0.092920 0.03181
## m9 -6.939 1.819 -0.1514 0.047920
## m11 -3.329 1.335 -0.4459 0.005123 -0.3419
## m10 -8.653 2.051 -0.3044 0.033900 0.33180
## m12 -3.240 1.319 -0.8179 0.017940 -0.4382 0.18500
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m3
## m0
## m1
## m2
## m4
## m9 -0.09333
## m11 0.02612
## m10 -0.10120 0.4450
## m12 0.02139 0.9812
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 family
## m3 NB(1.8576)
## m0 NB(1.5417)
## m1 NB(1.5809)
## m2 NB(1.5472)
## m4 NB(1.5473)
## m9 NB(1.5886)
## m11 0.10640 NB(1.8814)
## m10 NB(1.6094)
## m12 0.09483 0.1178 NB(2.008)
## init.theta df logLik AICc delta weight
## m3 1.86 5 -195.253 403.0 0.00 0.466
## m0 1.54 3 -198.487 403.9 0.89 0.299
## m1 1.58 4 -198.044 405.7 2.68 0.122
## m2 1.55 4 -198.424 406.4 3.44 0.083
## m4 1.55 5 -198.423 409.3 6.34 0.020
## m9 1.59 6 -197.959 411.6 8.56 0.006
## m11 1.88 8 -195.035 412.9 9.92 0.003
## m10 1.61 8 -197.730 418.3 15.31 0.000
## m12 2.01 11 -193.917 424.5 21.49 0.000
## Abbreviations:
## family: NB(1.5417) = 'Negative Binomial(1.5417)',
## NB(1.5472) = 'Negative Binomial(1.5472)',
## NB(1.5473) = 'Negative Binomial(1.5473)',
## NB(1.5809) = 'Negative Binomial(1.5809)',
## NB(1.5886) = 'Negative Binomial(1.5886)',
## NB(1.6094) = 'Negative Binomial(1.6094)',
## NB(1.8576) = 'Negative Binomial(1.8576)',
## NB(1.8814) = 'Negative Binomial(1.8814)',
## NB(2.008) = 'Negative Binomial(2.008)'
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m3 -4.168 1.456 -0.4422 -0.3343
## m0 -5.301 1.589
## m1 -7.332 1.876 -0.1573
## m2 -4.596 1.486 0.094340
## m4 -4.798 1.514 0.092910 0.0317
## m9 -6.939 1.819 -0.1514 0.047930
## m11 -3.329 1.335 -0.4459 0.005146 -0.3419
## m10 -8.651 2.051 -0.3042 0.033910 0.3315
## m12 -3.240 1.319 -0.8181 0.017890 -0.4383 0.1850
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m3
## m0
## m1
## m2
## m4
## m9 -0.09331
## m11 0.02612
## m10 -0.10120 0.4447
## m12 0.02145 0.9817
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik
## m3 4 -195.254
## m0 2 -198.812
## m1 3 -198.285
## m2 3 -198.737
## m4 4 -198.735
## m9 5 -198.186
## m11 0.10640 7 -195.036
## m10 7 -197.919
## m12 0.09489 0.1179 10 -193.965
## AICc delta weight
## m3 400.1 0.00 0.578
## m0 402.1 1.96 0.217
## m1 403.5 3.39 0.106
## m2 404.4 4.29 0.068
## m4 407.1 6.96 0.018
## m9 408.9 8.76 0.007
## m11 409.2 9.06 0.006
## m10 414.9 14.82 0.000
## m12 419.5 19.40 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 6.487 -0.16260
## m4 7.224 -0.23430 0.3969 -1.293
## m2 5.226 0.06602 0.4385
## m1 6.728 -0.20270 0.136900
## m3 7.086 -0.25470 0.037080 -0.08993
## m9 5.825 -0.04409 0.087330 0.5179
## m10 9.796 -0.69340 0.460800 0.4672 -1.653
## m11 6.268 -0.10960 -0.015850 0.2986 -0.10770
## m12 9.701 -0.62920 -0.003657 0.2871 -0.36460 -2.229
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m4
## m2
## m1
## m3
## m9 0.3040
## m10 0.3856 -1.1790
## m11 0.5354
## m12 0.5810 -0.1131
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 family
## m0 NB(1.1765)
## m4 NB(1.3622)
## m2 NB(1.234)
## m1 NB(1.1997)
## m3 NB(1.2098)
## m9 NB(1.2876)
## m10 NB(1.5568)
## m11 0.2162 NB(1.3283)
## m12 0.1957 0.8084 NB(1.685)
## init.theta df logLik AICc delta weight
## m0 1.18 3 -198.026 403.0 0.00 0.351
## m4 1.36 5 -195.587 403.7 0.70 0.247
## m2 1.23 4 -197.222 404.0 1.07 0.206
## m1 1.2 4 -197.697 405.0 2.02 0.128
## m3 1.21 5 -197.555 407.6 4.63 0.035
## m9 1.29 6 -196.512 408.7 5.70 0.020
## m10 1.56 8 -193.435 409.7 6.75 0.012
## m11 1.33 8 -195.999 414.9 11.88 0.001
## m12 1.69 11 -192.201 421.1 18.09 0.000
## Abbreviations:
## family: NB(1.1765) = 'Negative Binomial(1.1765)',
## NB(1.1997) = 'Negative Binomial(1.1997)',
## NB(1.2098) = 'Negative Binomial(1.2098)',
## NB(1.234) = 'Negative Binomial(1.234)',
## NB(1.2876) = 'Negative Binomial(1.2876)',
## NB(1.3283) = 'Negative Binomial(1.3283)',
## NB(1.3622) = 'Negative Binomial(1.3622)',
## NB(1.5568) = 'Negative Binomial(1.5568)',
## NB(1.685) = 'Negative Binomial(1.685)'
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 6.487 -0.16260
## m4 7.225 -0.23440 0.3968 -1.293
## m2 5.226 0.06603 0.4386
## m1 6.728 -0.20270 0.136900
## m3 7.086 -0.25460 0.037080 -0.08992
## m9 5.826 -0.04420 0.087320 0.5180
## m10 9.803 -0.69470 0.461100 0.4673 -1.654
## m11 6.269 -0.10980 -0.015850 0.2987 -0.10770
## m12 9.713 -0.63150 -0.003589 0.2872 -0.36450 -2.230
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m4
## m2
## m1
## m3
## m9 0.3041
## m10 0.3858 -1.1800
## m11 0.5354
## m12 0.5811 -0.1138
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik
## m0 2 -198.026
## m4 4 -195.749
## m2 3 -197.238
## m1 3 -197.699
## m3 4 -197.560
## m9 5 -196.573
## m10 7 -194.000
## m11 0.2162 7 -196.110
## m12 0.1956 0.8081 10 -193.099
## AICc delta weight
## m0 400.5 0.00 0.331
## m4 401.1 0.60 0.245
## m2 401.4 0.90 0.211
## m1 402.3 1.82 0.133
## m3 404.7 4.22 0.040
## m9 405.6 5.15 0.025
## m10 407.1 6.59 0.012
## m11 411.3 10.81 0.001
## m12 417.8 17.28 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 6.487 -0.16260
## m4 7.224 -0.23430 0.3969 -1.293
## m2 5.226 0.06602 0.4385
## m1 6.728 -0.20270 0.136900
## m3 7.086 -0.25470 0.037080 -0.08993
## m9 5.825 -0.04409 0.087330 0.5179
## m10 9.796 -0.69340 0.460800 0.4672 -1.653
## m11 6.268 -0.10960 -0.015850 0.2986 -0.10770
## m12 9.701 -0.62920 -0.003657 0.2871 -0.36460 -2.229
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m4
## m2
## m1
## m3
## m9 0.3040
## m10 0.3856 -1.1790
## m11 0.5354
## m12 0.5810 -0.1131
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 family
## m0 NB(1.1765)
## m4 NB(1.3622)
## m2 NB(1.234)
## m1 NB(1.1997)
## m3 NB(1.2098)
## m9 NB(1.2876)
## m10 NB(1.5568)
## m11 0.2162 NB(1.3283)
## m12 0.1957 0.8084 NB(1.685)
## init.theta df logLik AICc delta weight
## m0 1.18 3 -198.026 403.0 0.00 0.351
## m4 1.36 5 -195.587 403.7 0.70 0.247
## m2 1.23 4 -197.222 404.0 1.07 0.206
## m1 1.2 4 -197.697 405.0 2.02 0.128
## m3 1.21 5 -197.555 407.6 4.63 0.035
## m9 1.29 6 -196.512 408.7 5.70 0.020
## m10 1.56 8 -193.435 409.7 6.75 0.012
## m11 1.33 8 -195.999 414.9 11.88 0.001
## m12 1.69 11 -192.201 421.1 18.09 0.000
## Abbreviations:
## family: NB(1.1765) = 'Negative Binomial(1.1765)',
## NB(1.1997) = 'Negative Binomial(1.1997)',
## NB(1.2098) = 'Negative Binomial(1.2098)',
## NB(1.234) = 'Negative Binomial(1.234)',
## NB(1.2876) = 'Negative Binomial(1.2876)',
## NB(1.3283) = 'Negative Binomial(1.3283)',
## NB(1.3622) = 'Negative Binomial(1.3622)',
## NB(1.5568) = 'Negative Binomial(1.5568)',
## NB(1.685) = 'Negative Binomial(1.685)'
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 6.487 -0.16260
## m4 7.225 -0.23440 0.3968 -1.293
## m2 5.226 0.06603 0.4386
## m1 6.728 -0.20270 0.136900
## m3 7.086 -0.25460 0.037080 -0.08992
## m9 5.826 -0.04420 0.087320 0.5180
## m10 9.803 -0.69470 0.461100 0.4673 -1.654
## m11 6.269 -0.10980 -0.015850 0.2987 -0.10770
## m12 9.713 -0.63150 -0.003589 0.2872 -0.36450 -2.230
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m4
## m2
## m1
## m3
## m9 0.3041
## m10 0.3858 -1.1800
## m11 0.5354
## m12 0.5811 -0.1138
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik
## m0 2 -198.026
## m4 4 -195.749
## m2 3 -197.238
## m1 3 -197.699
## m3 4 -197.560
## m9 5 -196.573
## m10 7 -194.000
## m11 0.2162 7 -196.110
## m12 0.1956 0.8081 10 -193.099
## AICc delta weight
## m0 400.5 0.00 0.331
## m4 401.1 0.60 0.245
## m2 401.4 0.90 0.211
## m1 402.3 1.82 0.133
## m3 404.7 4.22 0.040
## m9 405.6 5.15 0.025
## m10 407.1 6.59 0.012
## m11 411.3 10.81 0.001
## m12 417.8 17.28 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 -0.8582 1.166
## m1 -1.0040 1.186 -0.1432
## m2 -1.0470 1.201 0.2713
## m4 -0.9280 1.211 0.2717 -0.5582
## m3 -1.1270 1.218 -0.1990 -0.04949
## m9 -2.9080 1.550 -0.1682 0.3482
## m10 -2.9230 1.574 -0.3441 0.3493 -0.4002
## m11 -2.6360 1.508 -0.2422 0.2794 -0.06667
## m12 -9.1260 2.873 -1.2480 0.4709 -0.76970 -1.8570
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m1
## m2
## m4
## m3
## m9 0.1904
## m10 0.1893 0.4891
## m11 0.2558
## m12 0.3493 2.7890
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 family
## m0 NB(1.0874)
## m1 NB(1.1113)
## m2 NB(1.1102)
## m4 NB(1.1286)
## m3 NB(1.1143)
## m9 NB(1.1533)
## m10 NB(1.1838)
## m11 0.06213 NB(1.1621)
## m12 -0.01626 2.018 NB(1.418)
## init.theta df logLik AICc delta weight
## m0 1.09 3 -184.711 376.3 0.00 0.502
## m1 1.11 4 -184.346 378.3 1.95 0.190
## m2 1.11 4 -184.363 378.3 1.98 0.186
## m4 1.13 5 -184.089 380.7 4.33 0.058
## m3 1.11 5 -184.300 381.1 4.75 0.047
## m9 1.15 6 -183.725 383.1 6.76 0.017
## m10 1.18 8 -183.292 389.4 13.10 0.001
## m11 1.16 8 -183.599 390.1 13.71 0.001
## m12 1.42 11 -180.262 397.2 20.85 0.000
## Abbreviations:
## family: NB(1.0874) = 'Negative Binomial(1.0874)',
## NB(1.1102) = 'Negative Binomial(1.1102)',
## NB(1.1113) = 'Negative Binomial(1.1113)',
## NB(1.1143) = 'Negative Binomial(1.1143)',
## NB(1.1286) = 'Negative Binomial(1.1286)',
## NB(1.1533) = 'Negative Binomial(1.1533)',
## NB(1.1621) = 'Negative Binomial(1.1621)',
## NB(1.1838) = 'Negative Binomial(1.1838)',
## NB(1.418) = 'Negative Binomial(1.418)'
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 -0.8582 1.166
## m1 -1.0040 1.186 -0.1432
## m2 -1.0470 1.201 0.2713
## m4 -0.9279 1.211 0.2717 -0.5582
## m3 -1.1270 1.218 -0.1990 -0.04949
## m9 -2.9090 1.550 -0.1682 0.3483
## m10 -2.9260 1.575 -0.3445 0.3494 -0.4001
## m11 -2.6400 1.509 -0.2423 0.2796 -0.06672
## m12 -9.2690 2.901 -1.2620 0.4769 -0.77780 -1.8680
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m1
## m2
## m4
## m3
## m9 0.1904
## m10 0.1894 0.4898
## m11 0.2557
## m12 0.3498 2.8170
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik
## m0 2 -184.711
## m1 3 -184.349
## m2 3 -184.365
## m4 4 -184.099
## m3 4 -184.304
## m9 5 -183.751
## m10 7 -183.347
## m11 0.06196 7 -183.632
## m12 -0.01927 2.037 10 -180.773
## AICc delta weight
## m0 373.9 0.00 0.469
## m1 375.6 1.75 0.195
## m2 375.7 1.79 0.192
## m4 377.8 3.93 0.066
## m3 378.2 4.34 0.054
## m9 380.0 6.13 0.022
## m10 385.8 11.92 0.001
## m11 386.4 12.49 0.001
## m12 393.1 19.26 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) cv.dpt prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m0 4.814 0.3203
## m2 4.494 0.4111 -1.182
## m5 4.408 0.3804 0.008276
## m4 4.351 0.4237 -0.004205
## m1 4.476 0.3645 0.00138
## m6 4.904 0.3106
## m3 4.761 0.3281 0.04299
## lst_wet family init.theta df logLik AICc delta weight
## m0 NB(0.958) 0.958 3 -230.574 468.1 0.00 0.374
## m2 NB(0.9668) 0.967 4 -230.411 470.4 2.35 0.115
## m5 NB(0.9625) 0.962 4 -230.490 470.6 2.51 0.107
## m4 NB(0.9608) 0.961 4 -230.522 470.6 2.57 0.103
## m1 NB(0.96) 0.96 4 -230.536 470.7 2.60 0.102
## m6 -0.002388 NB(0.9589) 0.959 4 -230.556 470.7 2.64 0.100
## m3 NB(0.958) 0.958 4 -230.573 470.7 2.68 0.098
## Abbreviations:
## family: NB(0.958) = 'Negative Binomial(0.958)',
## NB(0.9589) = 'Negative Binomial(0.9589)',
## NB(0.96) = 'Negative Binomial(0.96)',
## NB(0.9608) = 'Negative Binomial(0.9608)',
## NB(0.9625) = 'Negative Binomial(0.9625)',
## NB(0.9668) = 'Negative Binomial(0.9668)'
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m2 4.494 0.4111 -1.182
## m5 4.408 0.3804 0.008275
## m4 4.351 0.4236 -0.004204
## m1 4.476 0.00138 0.3645
## m6 4.904 0.3106
## m3 4.761 0.3281 0.04299
## lst_wet df logLik AICc delta weight
## m2 3 -230.412 467.7 0.00 0.184
## m5 3 -230.490 467.9 0.16 0.171
## m4 3 -230.522 468.0 0.22 0.165
## m1 3 -230.536 468.0 0.25 0.163
## m6 -0.002389 3 -230.556 468.0 0.29 0.160
## m3 3 -230.573 468.1 0.32 0.157
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) cv.dpt prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m0 4.814 0.3203
## m2 4.494 0.4111 -1.182
## m5 4.408 0.3804 0.008276
## m4 4.351 0.4237 -0.004205
## m1 4.476 0.3645 0.00138
## m6 4.904 0.3106
## m3 4.761 0.3281 0.04299
## lst_wet family init.theta df logLik AICc delta weight
## m0 NB(0.958) 0.958 3 -230.574 468.1 0.00 0.374
## m2 NB(0.9668) 0.967 4 -230.411 470.4 2.35 0.115
## m5 NB(0.9625) 0.962 4 -230.490 470.6 2.51 0.107
## m4 NB(0.9608) 0.961 4 -230.522 470.6 2.57 0.103
## m1 NB(0.96) 0.96 4 -230.536 470.7 2.60 0.102
## m6 -0.002388 NB(0.9589) 0.959 4 -230.556 470.7 2.64 0.100
## m3 NB(0.958) 0.958 4 -230.573 470.7 2.68 0.098
## Abbreviations:
## family: NB(0.958) = 'Negative Binomial(0.958)',
## NB(0.9589) = 'Negative Binomial(0.9589)',
## NB(0.96) = 'Negative Binomial(0.96)',
## NB(0.9608) = 'Negative Binomial(0.9608)',
## NB(0.9625) = 'Negative Binomial(0.9625)',
## NB(0.9668) = 'Negative Binomial(0.9668)'
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m2 4.494 0.4111 -1.182
## m5 4.408 0.3804 0.008275
## m4 4.351 0.4236 -0.004204
## m1 4.476 0.00138 0.3645
## m6 4.904 0.3106
## m3 4.761 0.3281 0.04299
## lst_wet df logLik AICc delta weight
## m2 3 -230.412 467.7 0.00 0.184
## m5 3 -230.490 467.9 0.16 0.171
## m4 3 -230.522 468.0 0.22 0.165
## m1 3 -230.536 468.0 0.25 0.163
## m6 -0.002389 3 -230.556 468.0 0.29 0.160
## m3 3 -230.573 468.1 0.32 0.157
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) cv.dpt prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m0 4.814 0.3203
## m2 4.494 0.4111 -1.182
## m5 4.408 0.3804 0.008276
## m4 4.351 0.4237 -0.004205
## m1 4.476 0.3645 0.00138
## m6 4.904 0.3106
## m3 4.761 0.3281 0.04299
## lst_wet family init.theta df logLik AICc delta weight
## m0 NB(0.958) 0.958 3 -230.574 468.1 0.00 0.374
## m2 NB(0.9668) 0.967 4 -230.411 470.4 2.35 0.115
## m5 NB(0.9625) 0.962 4 -230.490 470.6 2.51 0.107
## m4 NB(0.9608) 0.961 4 -230.522 470.6 2.57 0.103
## m1 NB(0.96) 0.96 4 -230.536 470.7 2.60 0.102
## m6 -0.002388 NB(0.9589) 0.959 4 -230.556 470.7 2.64 0.100
## m3 NB(0.958) 0.958 4 -230.573 470.7 2.68 0.098
## Abbreviations:
## family: NB(0.958) = 'Negative Binomial(0.958)',
## NB(0.9589) = 'Negative Binomial(0.9589)',
## NB(0.96) = 'Negative Binomial(0.96)',
## NB(0.9608) = 'Negative Binomial(0.9608)',
## NB(0.9625) = 'Negative Binomial(0.9625)',
## NB(0.9668) = 'Negative Binomial(0.9668)'
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m2 4.494 0.4111 -1.182
## m5 4.408 0.3804 0.008275
## m4 4.351 0.4236 -0.004204
## m1 4.476 0.00138 0.3645
## m6 4.904 0.3106
## m3 4.761 0.3281 0.04299
## lst_wet df logLik AICc delta weight
## m2 3 -230.412 467.7 0.00 0.184
## m5 3 -230.490 467.9 0.16 0.171
## m4 3 -230.522 468.0 0.22 0.165
## m1 3 -230.536 468.0 0.25 0.163
## m6 -0.002389 3 -230.556 468.0 0.29 0.160
## m3 3 -230.573 468.1 0.32 0.157
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) cv.dpt prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m0 4.814 0.3203
## m2 4.494 0.4111 -1.182
## m5 4.408 0.3804 0.008276
## m4 4.351 0.4237 -0.004205
## m1 4.476 0.3645 0.00138
## m6 4.904 0.3106
## m3 4.761 0.3281 0.04299
## lst_wet family init.theta df logLik AICc delta weight
## m0 NB(0.958) 0.958 3 -230.574 468.1 0.00 0.374
## m2 NB(0.9668) 0.967 4 -230.411 470.4 2.35 0.115
## m5 NB(0.9625) 0.962 4 -230.490 470.6 2.51 0.107
## m4 NB(0.9608) 0.961 4 -230.522 470.6 2.57 0.103
## m1 NB(0.96) 0.96 4 -230.536 470.7 2.60 0.102
## m6 -0.002388 NB(0.9589) 0.959 4 -230.556 470.7 2.64 0.100
## m3 NB(0.958) 0.958 4 -230.573 470.7 2.68 0.098
## Abbreviations:
## family: NB(0.958) = 'Negative Binomial(0.958)',
## NB(0.9589) = 'Negative Binomial(0.9589)',
## NB(0.96) = 'Negative Binomial(0.96)',
## NB(0.9608) = 'Negative Binomial(0.9608)',
## NB(0.9625) = 'Negative Binomial(0.9625)',
## NB(0.9668) = 'Negative Binomial(0.9668)'
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m2 4.494 0.4111 -1.182
## m5 4.408 0.3804 0.008275
## m4 4.351 0.4236 -0.004204
## m1 4.476 0.00138 0.3645
## m6 4.904 0.3106
## m3 4.761 0.3281 0.04299
## lst_wet df logLik AICc delta weight
## m2 3 -230.412 467.7 0.00 0.184
## m5 3 -230.490 467.9 0.16 0.171
## m4 3 -230.522 468.0 0.22 0.165
## m1 3 -230.536 468.0 0.25 0.163
## m6 -0.002389 3 -230.556 468.0 0.29 0.160
## m3 3 -230.573 468.1 0.32 0.157
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) cv.dpt prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m0 4.814 0.3203
## m2 4.494 0.4111 -1.182
## m5 4.408 0.3804 0.008276
## m4 4.351 0.4237 -0.004205
## m1 4.476 0.3645 0.00138
## m6 4.904 0.3106
## m3 4.761 0.3281 0.04299
## lst_wet family init.theta df logLik AICc delta weight
## m0 NB(0.958) 0.958 3 -230.574 468.1 0.00 0.374
## m2 NB(0.9668) 0.967 4 -230.411 470.4 2.35 0.115
## m5 NB(0.9625) 0.962 4 -230.490 470.6 2.51 0.107
## m4 NB(0.9608) 0.961 4 -230.522 470.6 2.57 0.103
## m1 NB(0.96) 0.96 4 -230.536 470.7 2.60 0.102
## m6 -0.002388 NB(0.9589) 0.959 4 -230.556 470.7 2.64 0.100
## m3 NB(0.958) 0.958 4 -230.573 470.7 2.68 0.098
## Abbreviations:
## family: NB(0.958) = 'Negative Binomial(0.958)',
## NB(0.9589) = 'Negative Binomial(0.9589)',
## NB(0.96) = 'Negative Binomial(0.96)',
## NB(0.9608) = 'Negative Binomial(0.9608)',
## NB(0.9625) = 'Negative Binomial(0.9625)',
## NB(0.9668) = 'Negative Binomial(0.9668)'
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m2 4.494 0.4111 -1.182
## m5 4.408 0.3804 0.008275
## m4 4.351 0.4236 -0.004204
## m1 4.476 0.00138 0.3645
## m6 4.904 0.3106
## m3 4.761 0.3281 0.04299
## lst_wet df logLik AICc delta weight
## m2 3 -230.412 467.7 0.00 0.184
## m5 3 -230.490 467.9 0.16 0.171
## m4 3 -230.522 468.0 0.22 0.165
## m1 3 -230.536 468.0 0.25 0.163
## m6 -0.002389 3 -230.556 468.0 0.29 0.160
## m3 3 -230.573 468.1 0.32 0.157
## Models ranked by AICc(x)
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in glm.nb(y ~ log(maxvol) + prop.driedout.days, data = dataset):
## alternation limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in glm.nb(y ~ log(maxvol) + long_dry, data = dataset): alternation
## limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Model selection table
## (Int) log(mxv) cv.dpt prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m4 6.272 -0.1847 -0.0172
## m3 6.794 -0.4009 0.6532
## m5 7.283 -0.4765 0.02156
## m6 7.185 -0.3982
## m2 7.727 -0.5163 -0.6563
## m1 7.913 -0.5548 -0.0003594
## m0 7.682 -0.5211
## lst_wet family init.theta df logLik AICc delta weight
## m4 NB(848942.2) 849000 4 -1847.833 3705.3 0.00 1
## m3 NB(802495.6) 802000 4 -1933.702 3877.0 171.74 0
## m5 NB(833072.8) 833000 4 -1955.970 3921.5 216.27 0
## m6 -0.008741 NB(837260.2) 837000 4 -1959.344 3928.3 223.02 0
## m2 NB(844577.2) 845000 4 -1978.860 3967.3 262.06 0
## m1 NB(850838) 851000 4 -1979.863 3969.3 264.06 0
## m0 NB(846723.8) 847000 3 -1982.630 3972.2 266.92 0
## Abbreviations:
## family: NB(802495.6) = 'Negative Binomial(802495.6)',
## NB(833072.8) = 'Negative Binomial(833072.8)',
## NB(837260.2) = 'Negative Binomial(837260.2)',
## NB(844577.2) = 'Negative Binomial(844577.2)',
## NB(846723.8) = 'Negative Binomial(846723.8)',
## NB(848942.2) = 'Negative Binomial(848942.2)',
## NB(850838) = 'Negative Binomial(850838)'
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m4 6.272 -0.1847 -0.0172
## m3 6.794 -0.4009 0.6532
## m5 7.283 -0.4765 0.02156
## m6 7.185 -0.3982
## m2 7.727 -0.5163 -0.6563
## m1 7.913 -0.0003594 -0.5548
## lst_wet df logLik AICc delta weight
## m4 3 -1847.833 3702.6 0.00 1
## m3 3 -1933.715 3874.4 171.76 0
## m5 3 -1955.975 3918.9 216.28 0
## m6 -0.008741 3 -1959.347 3925.6 223.03 0
## m2 3 -1978.861 3964.6 262.06 0
## m1 3 -1979.863 3966.6 264.06 0
## Models ranked by AICc(x)
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in glm.nb(y ~ log(maxvol) + prop.driedout.days, data = dataset):
## alternation limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in glm.nb(y ~ log(maxvol) + long_dry, data = dataset): alternation
## limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Model selection table
## (Int) log(mxv) cv.dpt prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m4 6.272 -0.1847 -0.0172
## m3 6.794 -0.4009 0.6532
## m5 7.283 -0.4765 0.02156
## m6 7.185 -0.3982
## m2 7.727 -0.5163 -0.6563
## m1 7.913 -0.5548 -0.0003594
## m0 7.682 -0.5211
## lst_wet family init.theta df logLik AICc delta weight
## m4 NB(848942.2) 849000 4 -1847.833 3705.3 0.00 1
## m3 NB(802495.6) 802000 4 -1933.702 3877.0 171.74 0
## m5 NB(833072.8) 833000 4 -1955.970 3921.5 216.27 0
## m6 -0.008741 NB(837260.2) 837000 4 -1959.344 3928.3 223.02 0
## m2 NB(844577.2) 845000 4 -1978.860 3967.3 262.06 0
## m1 NB(850838) 851000 4 -1979.863 3969.3 264.06 0
## m0 NB(846723.8) 847000 3 -1982.630 3972.2 266.92 0
## Abbreviations:
## family: NB(802495.6) = 'Negative Binomial(802495.6)',
## NB(833072.8) = 'Negative Binomial(833072.8)',
## NB(837260.2) = 'Negative Binomial(837260.2)',
## NB(844577.2) = 'Negative Binomial(844577.2)',
## NB(846723.8) = 'Negative Binomial(846723.8)',
## NB(848942.2) = 'Negative Binomial(848942.2)',
## NB(850838) = 'Negative Binomial(850838)'
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m4 6.272 -0.1847 -0.0172
## m3 6.794 -0.4009 0.6532
## m5 7.283 -0.4765 0.02156
## m6 7.185 -0.3982
## m2 7.727 -0.5163 -0.6563
## m1 7.913 -0.0003594 -0.5548
## lst_wet df logLik AICc delta weight
## m4 3 -1847.833 3702.6 0.00 1
## m3 3 -1933.715 3874.4 171.76 0
## m5 3 -1955.975 3918.9 216.28 0
## m6 -0.008741 3 -1959.347 3925.6 223.03 0
## m2 3 -1978.861 3964.6 262.06 0
## m1 3 -1979.863 3966.6 264.06 0
## Models ranked by AICc(x)
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in glm.nb(y ~ log(maxvol) + prop.driedout.days, data = dataset):
## alternation limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in glm.nb(y ~ log(maxvol) + long_dry, data = dataset): alternation
## limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Model selection table
## (Int) log(mxv) cv.dpt prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m4 6.272 -0.1847 -0.0172
## m3 6.794 -0.4009 0.6532
## m5 7.283 -0.4765 0.02156
## m6 7.185 -0.3982
## m2 7.727 -0.5163 -0.6563
## m1 7.913 -0.5548 -0.0003594
## m0 7.682 -0.5211
## lst_wet family init.theta df logLik AICc delta weight
## m4 NB(848942.2) 849000 4 -1847.833 3705.3 0.00 1
## m3 NB(802495.6) 802000 4 -1933.702 3877.0 171.74 0
## m5 NB(833072.8) 833000 4 -1955.970 3921.5 216.27 0
## m6 -0.008741 NB(837260.2) 837000 4 -1959.344 3928.3 223.02 0
## m2 NB(844577.2) 845000 4 -1978.860 3967.3 262.06 0
## m1 NB(850838) 851000 4 -1979.863 3969.3 264.06 0
## m0 NB(846723.8) 847000 3 -1982.630 3972.2 266.92 0
## Abbreviations:
## family: NB(802495.6) = 'Negative Binomial(802495.6)',
## NB(833072.8) = 'Negative Binomial(833072.8)',
## NB(837260.2) = 'Negative Binomial(837260.2)',
## NB(844577.2) = 'Negative Binomial(844577.2)',
## NB(846723.8) = 'Negative Binomial(846723.8)',
## NB(848942.2) = 'Negative Binomial(848942.2)',
## NB(850838) = 'Negative Binomial(850838)'
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m4 6.272 -0.1847 -0.0172
## m3 6.794 -0.4009 0.6532
## m5 7.283 -0.4765 0.02156
## m6 7.185 -0.3982
## m2 7.727 -0.5163 -0.6563
## m1 7.913 -0.0003594 -0.5548
## lst_wet df logLik AICc delta weight
## m4 3 -1847.833 3702.6 0.00 1
## m3 3 -1933.715 3874.4 171.76 0
## m5 3 -1955.975 3918.9 216.28 0
## m6 -0.008741 3 -1959.347 3925.6 223.03 0
## m2 3 -1978.861 3964.6 262.06 0
## m1 3 -1979.863 3966.6 264.06 0
## Models ranked by AICc(x)
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in glm.nb(y ~ log(maxvol) + prop.driedout.days, data = dataset):
## alternation limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in glm.nb(y ~ log(maxvol) + long_dry, data = dataset): alternation
## limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Model selection table
## (Int) log(mxv) cv.dpt prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m4 6.272 -0.1847 -0.0172
## m3 6.794 -0.4009 0.6532
## m5 7.283 -0.4765 0.02156
## m6 7.185 -0.3982
## m2 7.727 -0.5163 -0.6563
## m1 7.913 -0.5548 -0.0003594
## m0 7.682 -0.5211
## lst_wet family init.theta df logLik AICc delta weight
## m4 NB(848942.2) 849000 4 -1847.833 3705.3 0.00 1
## m3 NB(802495.6) 802000 4 -1933.702 3877.0 171.74 0
## m5 NB(833072.8) 833000 4 -1955.970 3921.5 216.27 0
## m6 -0.008741 NB(837260.2) 837000 4 -1959.344 3928.3 223.02 0
## m2 NB(844577.2) 845000 4 -1978.860 3967.3 262.06 0
## m1 NB(850838) 851000 4 -1979.863 3969.3 264.06 0
## m0 NB(846723.8) 847000 3 -1982.630 3972.2 266.92 0
## Abbreviations:
## family: NB(802495.6) = 'Negative Binomial(802495.6)',
## NB(833072.8) = 'Negative Binomial(833072.8)',
## NB(837260.2) = 'Negative Binomial(837260.2)',
## NB(844577.2) = 'Negative Binomial(844577.2)',
## NB(846723.8) = 'Negative Binomial(846723.8)',
## NB(848942.2) = 'Negative Binomial(848942.2)',
## NB(850838) = 'Negative Binomial(850838)'
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m4 6.272 -0.1847 -0.0172
## m3 6.794 -0.4009 0.6532
## m5 7.283 -0.4765 0.02156
## m6 7.185 -0.3982
## m2 7.727 -0.5163 -0.6563
## m1 7.913 -0.0003594 -0.5548
## lst_wet df logLik AICc delta weight
## m4 3 -1847.833 3702.6 0.00 1
## m3 3 -1933.715 3874.4 171.76 0
## m5 3 -1955.975 3918.9 216.28 0
## m6 -0.008741 3 -1959.347 3925.6 223.03 0
## m2 3 -1978.861 3964.6 262.06 0
## m1 3 -1979.863 3966.6 264.06 0
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) cv.dpt prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m2 -6.909 1.882 -7.149
## m6 -4.983 1.484
## m0 -5.300 1.589
## m5 -7.110 1.861 -0.05027
## m4 -5.431 1.730 -0.01351
## m3 -6.323 1.744 -1.478
## m1 -6.188 1.741 -0.004475
## lst_wet family init.theta df logLik AICc delta weight
## m2 NB(1.7073) 1.71 4 -196.702 403.0 0.00 0.258
## m6 0.01244 NB(1.6775) 1.68 4 -197.009 403.6 0.61 0.190
## m0 NB(1.5417) 1.54 3 -198.487 403.9 0.89 0.165
## m5 NB(1.6371) 1.64 4 -197.427 404.5 1.45 0.125
## m4 NB(1.6274) 1.63 4 -197.533 404.7 1.66 0.112
## m3 NB(1.6106) 1.61 4 -197.717 405.0 2.03 0.093
## m1 NB(1.5666) 1.57 4 -198.204 406.0 3.00 0.057
## Abbreviations:
## family: NB(1.5417) = 'Negative Binomial(1.5417)',
## NB(1.5666) = 'Negative Binomial(1.5666)',
## NB(1.6106) = 'Negative Binomial(1.6106)',
## NB(1.6274) = 'Negative Binomial(1.6274)',
## NB(1.6371) = 'Negative Binomial(1.6371)',
## NB(1.6775) = 'Negative Binomial(1.6775)',
## NB(1.7073) = 'Negative Binomial(1.7073)'
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m2 -6.909 1.882 -7.149
## m6 -4.983 1.484
## m5 -7.111 1.861 -0.05026
## m4 -5.431 1.730 -0.0135
## m3 -6.324 1.745 -1.478
## m1 -6.188 -0.004472 1.741
## lst_wet df logLik AICc delta weight
## m2 3 -196.702 400.3 0.00 0.313
## m6 0.01244 3 -197.012 400.9 0.62 0.229
## m5 3 -197.443 401.8 1.48 0.149
## m4 3 -197.554 402.0 1.70 0.133
## m3 3 -197.748 402.4 2.09 0.110
## m1 3 -198.273 403.5 3.14 0.065
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) cv.dpt prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m2 -6.909 1.882 -7.149
## m6 -4.983 1.484
## m0 -5.300 1.589
## m5 -7.110 1.861 -0.05027
## m4 -5.431 1.730 -0.01351
## m3 -6.323 1.744 -1.478
## m1 -6.188 1.741 -0.004475
## lst_wet family init.theta df logLik AICc delta weight
## m2 NB(1.7073) 1.71 4 -196.702 403.0 0.00 0.258
## m6 0.01244 NB(1.6775) 1.68 4 -197.009 403.6 0.61 0.190
## m0 NB(1.5417) 1.54 3 -198.487 403.9 0.89 0.165
## m5 NB(1.6371) 1.64 4 -197.427 404.5 1.45 0.125
## m4 NB(1.6274) 1.63 4 -197.533 404.7 1.66 0.112
## m3 NB(1.6106) 1.61 4 -197.717 405.0 2.03 0.093
## m1 NB(1.5666) 1.57 4 -198.204 406.0 3.00 0.057
## Abbreviations:
## family: NB(1.5417) = 'Negative Binomial(1.5417)',
## NB(1.5666) = 'Negative Binomial(1.5666)',
## NB(1.6106) = 'Negative Binomial(1.6106)',
## NB(1.6274) = 'Negative Binomial(1.6274)',
## NB(1.6371) = 'Negative Binomial(1.6371)',
## NB(1.6775) = 'Negative Binomial(1.6775)',
## NB(1.7073) = 'Negative Binomial(1.7073)'
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m2 -6.909 1.882 -7.149
## m6 -4.983 1.484
## m5 -7.111 1.861 -0.05026
## m4 -5.431 1.730 -0.0135
## m3 -6.324 1.745 -1.478
## m1 -6.188 -0.004472 1.741
## lst_wet df logLik AICc delta weight
## m2 3 -196.702 400.3 0.00 0.313
## m6 0.01244 3 -197.012 400.9 0.62 0.229
## m5 3 -197.443 401.8 1.48 0.149
## m4 3 -197.554 402.0 1.70 0.133
## m3 3 -197.748 402.4 2.09 0.110
## m1 3 -198.273 403.5 3.14 0.065
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) cv.dpt prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m2 -6.909 1.882 -7.149
## m6 -4.983 1.484
## m0 -5.300 1.589
## m5 -7.110 1.861 -0.05027
## m4 -5.431 1.730 -0.01351
## m3 -6.323 1.744 -1.478
## m1 -6.188 1.741 -0.004475
## lst_wet family init.theta df logLik AICc delta weight
## m2 NB(1.7073) 1.71 4 -196.702 403.0 0.00 0.258
## m6 0.01244 NB(1.6775) 1.68 4 -197.009 403.6 0.61 0.190
## m0 NB(1.5417) 1.54 3 -198.487 403.9 0.89 0.165
## m5 NB(1.6371) 1.64 4 -197.427 404.5 1.45 0.125
## m4 NB(1.6274) 1.63 4 -197.533 404.7 1.66 0.112
## m3 NB(1.6106) 1.61 4 -197.717 405.0 2.03 0.093
## m1 NB(1.5666) 1.57 4 -198.204 406.0 3.00 0.057
## Abbreviations:
## family: NB(1.5417) = 'Negative Binomial(1.5417)',
## NB(1.5666) = 'Negative Binomial(1.5666)',
## NB(1.6106) = 'Negative Binomial(1.6106)',
## NB(1.6274) = 'Negative Binomial(1.6274)',
## NB(1.6371) = 'Negative Binomial(1.6371)',
## NB(1.6775) = 'Negative Binomial(1.6775)',
## NB(1.7073) = 'Negative Binomial(1.7073)'
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m2 -6.909 1.882 -7.149
## m6 -4.983 1.484
## m5 -7.111 1.861 -0.05026
## m4 -5.431 1.730 -0.0135
## m3 -6.324 1.745 -1.478
## m1 -6.188 -0.004472 1.741
## lst_wet df logLik AICc delta weight
## m2 3 -196.702 400.3 0.00 0.313
## m6 0.01244 3 -197.012 400.9 0.62 0.229
## m5 3 -197.443 401.8 1.48 0.149
## m4 3 -197.554 402.0 1.70 0.133
## m3 3 -197.748 402.4 2.09 0.110
## m1 3 -198.273 403.5 3.14 0.065
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) cv.dpt prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m2 7.191 -0.1980 -2.297
## m0 6.487 -0.1626
## m5 7.044 -0.2585 -0.04072
## m1 7.922 -0.3943 -0.007225
## m3 6.945 -0.2425 -1.322
## m6 6.778 -0.2099
## m4 6.859 -0.2609 0.003243
## lst_wet family init.theta df logLik AICc delta weight
## m2 NB(1.3246) 1.32 4 -196.042 401.7 0.00 0.440
## m0 NB(1.1765) 1.18 3 -198.026 403.0 1.29 0.231
## m5 NB(1.1868) 1.19 4 -197.878 405.4 3.67 0.070
## m1 NB(1.1859) 1.19 4 -197.892 405.4 3.70 0.069
## m3 NB(1.181) 1.18 4 -197.962 405.5 3.84 0.064
## m6 -0.007926 NB(1.1807) 1.18 4 -197.966 405.5 3.85 0.064
## m4 NB(1.1775) 1.18 4 -198.011 405.6 3.94 0.061
## Abbreviations:
## family: NB(1.1765) = 'Negative Binomial(1.1765)',
## NB(1.1775) = 'Negative Binomial(1.1775)',
## NB(1.1807) = 'Negative Binomial(1.1807)',
## NB(1.181) = 'Negative Binomial(1.181)',
## NB(1.1859) = 'Negative Binomial(1.1859)',
## NB(1.1868) = 'Negative Binomial(1.1868)',
## NB(1.3246) = 'Negative Binomial(1.3246)'
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m2 7.191 -0.1980 -2.297
## m5 7.044 -0.2585 -0.04072
## m1 7.922 -0.007225 -0.3943
## m3 6.944 -0.2425 -1.322
## m6 6.778 -0.2099
## m4 6.858 -0.2609 0.003241
## lst_wet df logLik AICc delta weight
## m2 3 -196.042 399.0 0.00 0.597
## m5 3 -197.976 402.9 3.87 0.086
## m1 3 -197.991 402.9 3.90 0.085
## m3 3 -198.069 403.1 4.05 0.079
## m6 -0.007926 3 -198.073 403.1 4.06 0.078
## m4 3 -198.124 403.2 4.16 0.074
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) cv.dpt prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m2 7.191 -0.1980 -2.297
## m0 6.487 -0.1626
## m5 7.044 -0.2585 -0.04072
## m1 7.922 -0.3943 -0.007225
## m3 6.945 -0.2425 -1.322
## m6 6.778 -0.2099
## m4 6.859 -0.2609 0.003243
## lst_wet family init.theta df logLik AICc delta weight
## m2 NB(1.3246) 1.32 4 -196.042 401.7 0.00 0.440
## m0 NB(1.1765) 1.18 3 -198.026 403.0 1.29 0.231
## m5 NB(1.1868) 1.19 4 -197.878 405.4 3.67 0.070
## m1 NB(1.1859) 1.19 4 -197.892 405.4 3.70 0.069
## m3 NB(1.181) 1.18 4 -197.962 405.5 3.84 0.064
## m6 -0.007926 NB(1.1807) 1.18 4 -197.966 405.5 3.85 0.064
## m4 NB(1.1775) 1.18 4 -198.011 405.6 3.94 0.061
## Abbreviations:
## family: NB(1.1765) = 'Negative Binomial(1.1765)',
## NB(1.1775) = 'Negative Binomial(1.1775)',
## NB(1.1807) = 'Negative Binomial(1.1807)',
## NB(1.181) = 'Negative Binomial(1.181)',
## NB(1.1859) = 'Negative Binomial(1.1859)',
## NB(1.1868) = 'Negative Binomial(1.1868)',
## NB(1.3246) = 'Negative Binomial(1.3246)'
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m2 7.191 -0.1980 -2.297
## m5 7.044 -0.2585 -0.04072
## m1 7.922 -0.007225 -0.3943
## m3 6.944 -0.2425 -1.322
## m6 6.778 -0.2099
## m4 6.858 -0.2609 0.003241
## lst_wet df logLik AICc delta weight
## m2 3 -196.042 399.0 0.00 0.597
## m5 3 -197.976 402.9 3.87 0.086
## m1 3 -197.991 402.9 3.90 0.085
## m3 3 -198.069 403.1 4.05 0.079
## m6 -0.007926 3 -198.073 403.1 4.06 0.078
## m4 3 -198.124 403.2 4.16 0.074
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) cv.dpt prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m0 -0.8582 1.1660
## m3 0.3005 0.9727 -0.7945
## m1 -0.1579 1.0880 -0.004958
## m6 -1.1450 1.2390
## m4 -0.2131 1.0010 0.01068
## m5 -1.2660 1.2340 0.03868
## m2 -0.8337 1.1630 -0.0319
## lst_wet family init.theta df logLik AICc delta weight
## m0 NB(1.0874) 1.09 3 -184.711 376.3 0.00 0.355
## m3 NB(1.1075) 1.11 4 -184.401 378.4 2.06 0.127
## m1 NB(1.1006) 1.1 4 -184.506 378.6 2.27 0.114
## m6 -0.00591 NB(1.0968) 1.1 4 -184.566 378.7 2.39 0.108
## m4 NB(1.0953) 1.1 4 -184.589 378.8 2.43 0.105
## m5 NB(1.0903) 1.09 4 -184.667 378.9 2.59 0.097
## m2 NB(1.0874) 1.09 4 -184.711 379.0 2.68 0.093
## Abbreviations:
## family: NB(1.0874) = 'Negative Binomial(1.0874)',
## NB(1.0903) = 'Negative Binomial(1.0903)',
## NB(1.0953) = 'Negative Binomial(1.0953)',
## NB(1.0968) = 'Negative Binomial(1.0968)',
## NB(1.1006) = 'Negative Binomial(1.1006)',
## NB(1.1075) = 'Negative Binomial(1.1075)'
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m3 0.3003 0.9727 -0.7946
## m1 -0.1576 -0.004959 1.0880
## m6 -1.1450 1.2390
## m4 -0.2131 1.0010 0.01068
## m5 -1.2660 1.2340 0.03868
## m2 -0.8336 1.1630 -0.03194
## lst_wet df logLik AICc delta weight
## m3 3 -184.403 375.7 0.00 0.197
## m1 3 -184.507 375.9 0.21 0.177
## m6 -0.00591 3 -184.566 376.1 0.33 0.167
## m4 3 -184.589 376.1 0.37 0.163
## m5 3 -184.667 376.3 0.53 0.151
## m2 3 -184.711 376.3 0.62 0.145
## Models ranked by AICc(x)
## Error in nrow(concord.out): object 'concord.out' not found
sites<-c("puertorico", "macae", "frenchguiana", "costarica")
concord.out4<-concord.magic(sites, "engulfer_bio", noargco13data, 10, 2, "nb")#all models work, but nost corr are neg
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning: glm.fit: algorithm did not converge
## Warning in glm.nb(y ~ log(maxvol) + log(mu.scalar) * (log(k.scalar) +
## I(log(k.scalar)^2)), : alternation limit reached
## Warning: glm.fit: fitted rates numerically 0 occurred
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning in glm.nb(y ~ log(maxvol) + (log(k.scalar) + I(log(k.scalar)^2))
## * : alternation limit reached
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 4.637 -0.6340
## m1 6.617 -0.9657 0.3999
## m2 4.816 -0.6647 -0.07571
## m3 6.862 -0.9355 -0.1256 -0.5627
## m4 8.644 -1.2550 -0.12780 -1.293
## m9 8.269 -1.2490 0.4293 -0.10200
## m10 10.510 -1.5870 1.5350 -0.12580 -1.144
## m11 9.161 -1.3280 -0.1070 -0.17620 -0.5725
## m12 18.050 -2.4300 0.2985 -0.30850 -9.4050 -6.367
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m1
## m2
## m3
## m4
## m9 0.2024
## m10 0.2371 -2.9160
## m11 0.2507
## m12 0.3960 -0.5263
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 family
## m0 NB(0.2198)
## m1 NB(0.2294)
## m2 NB(0.2199)
## m3 NB(0.2481)
## m4 NB(0.2265)
## m9 NB(0.2304)
## m10 NB(0.2356)
## m11 0.0585 NB(0.249)
## m12 0.1030 19.25 NB(0.3894)
## init.theta df logLik AICc delta weight
## m0 0.22 3 -55.168 117.3 0.00 0.526
## m1 0.229 4 -54.823 119.2 1.99 0.195
## m2 0.22 4 -55.163 119.9 2.67 0.139
## m3 0.248 5 -54.154 120.8 3.55 0.089
## m4 0.226 5 -54.935 122.4 5.11 0.041
## m9 0.23 6 -54.766 125.2 7.93 0.010
## m10 0.236 8 -53.917 130.7 13.43 0.001
## m11 0.249 8 -54.089 131.0 13.78 0.001
## m12 0.389 11 -50.234 137.1 19.88 0.000
## Abbreviations:
## family: NB(0.2198) = 'Negative Binomial(0.2198)',
## NB(0.2199) = 'Negative Binomial(0.2199)',
## NB(0.2265) = 'Negative Binomial(0.2265)',
## NB(0.2294) = 'Negative Binomial(0.2294)',
## NB(0.2304) = 'Negative Binomial(0.2304)',
## NB(0.2356) = 'Negative Binomial(0.2356)',
## NB(0.2481) = 'Negative Binomial(0.2481)',
## NB(0.249) = 'Negative Binomial(0.249)',
## NB(0.3894) = 'Negative Binomial(0.3894)'
## Models ranked by AICc(x)
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 4.637 -0.6340
## m1 6.640 -0.9696 0.4012
## m2 4.815 -0.6646 -0.07566
## m3 6.940 -0.9476 -0.1288 -0.5716
## m4 8.659 -1.2580 -0.12830 -1.295
## m9 8.328 -1.2590 0.4316 -0.10320
## m10 10.490 -1.5820 1.5630 -0.13040 -1.169
## m11 9.400 -1.3670 -0.1091 -0.18580 -0.5827
## m12 18.750 -2.5150 0.1782 -0.36720 -10.3000 -6.744
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m1
## m2
## m3
## m4
## m9 0.2043
## m10 0.2415 -2.9660
## m11 0.2602
## m12 0.4311 -0.2645
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik
## m0 2 -55.168
## m1 3 -54.829
## m2 3 -55.163
## m3 4 -54.199
## m4 4 -54.938
## m9 5 -54.773
## m10 7 -53.961
## m11 0.06286 7 -54.137
## m12 0.12240 21.06 10 -51.080
## AICc delta weight
## m0 114.8 0.00 0.494
## m1 116.6 1.80 0.201
## m2 117.2 2.47 0.144
## m3 118.0 3.22 0.099
## m4 119.5 4.70 0.047
## m9 122.0 7.27 0.013
## m10 127.0 12.23 0.001
## m11 127.4 12.58 0.001
## m12 133.7 18.96 0.000
## Models ranked by AICc(x)
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning: glm.fit: algorithm did not converge
## Warning in glm.nb(y ~ log(maxvol) + log(mu.scalar) * (log(k.scalar) +
## I(log(k.scalar)^2)), : alternation limit reached
## Warning: glm.fit: fitted rates numerically 0 occurred
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning in glm.nb(y ~ log(maxvol) + (log(k.scalar) + I(log(k.scalar)^2))
## * : alternation limit reached
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 4.637 -0.6340
## m1 6.617 -0.9657 0.3999
## m2 4.816 -0.6647 -0.07571
## m3 6.862 -0.9355 -0.1256 -0.5627
## m4 8.644 -1.2550 -0.12780 -1.293
## m9 8.269 -1.2490 0.4293 -0.10200
## m10 10.510 -1.5870 1.5350 -0.12580 -1.144
## m11 9.161 -1.3280 -0.1070 -0.17620 -0.5725
## m12 18.050 -2.4300 0.2985 -0.30850 -9.4050 -6.367
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m1
## m2
## m3
## m4
## m9 0.2024
## m10 0.2371 -2.9160
## m11 0.2507
## m12 0.3960 -0.5263
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 family
## m0 NB(0.2198)
## m1 NB(0.2294)
## m2 NB(0.2199)
## m3 NB(0.2481)
## m4 NB(0.2265)
## m9 NB(0.2304)
## m10 NB(0.2356)
## m11 0.0585 NB(0.249)
## m12 0.1030 19.25 NB(0.3894)
## init.theta df logLik AICc delta weight
## m0 0.22 3 -55.168 117.3 0.00 0.526
## m1 0.229 4 -54.823 119.2 1.99 0.195
## m2 0.22 4 -55.163 119.9 2.67 0.139
## m3 0.248 5 -54.154 120.8 3.55 0.089
## m4 0.226 5 -54.935 122.4 5.11 0.041
## m9 0.23 6 -54.766 125.2 7.93 0.010
## m10 0.236 8 -53.917 130.7 13.43 0.001
## m11 0.249 8 -54.089 131.0 13.78 0.001
## m12 0.389 11 -50.234 137.1 19.88 0.000
## Abbreviations:
## family: NB(0.2198) = 'Negative Binomial(0.2198)',
## NB(0.2199) = 'Negative Binomial(0.2199)',
## NB(0.2265) = 'Negative Binomial(0.2265)',
## NB(0.2294) = 'Negative Binomial(0.2294)',
## NB(0.2304) = 'Negative Binomial(0.2304)',
## NB(0.2356) = 'Negative Binomial(0.2356)',
## NB(0.2481) = 'Negative Binomial(0.2481)',
## NB(0.249) = 'Negative Binomial(0.249)',
## NB(0.3894) = 'Negative Binomial(0.3894)'
## Models ranked by AICc(x)
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 4.637 -0.6340
## m1 6.640 -0.9696 0.4012
## m2 4.815 -0.6646 -0.07566
## m3 6.940 -0.9476 -0.1288 -0.5716
## m4 8.659 -1.2580 -0.12830 -1.295
## m9 8.328 -1.2590 0.4316 -0.10320
## m10 10.490 -1.5820 1.5630 -0.13040 -1.169
## m11 9.400 -1.3670 -0.1091 -0.18580 -0.5827
## m12 18.750 -2.5150 0.1782 -0.36720 -10.3000 -6.744
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m1
## m2
## m3
## m4
## m9 0.2043
## m10 0.2415 -2.9660
## m11 0.2602
## m12 0.4311 -0.2645
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik
## m0 2 -55.168
## m1 3 -54.829
## m2 3 -55.163
## m3 4 -54.199
## m4 4 -54.938
## m9 5 -54.773
## m10 7 -53.961
## m11 0.06286 7 -54.137
## m12 0.12240 21.06 10 -51.080
## AICc delta weight
## m0 114.8 0.00 0.494
## m1 116.6 1.80 0.201
## m2 117.2 2.47 0.144
## m3 118.0 3.22 0.099
## m4 119.5 4.70 0.047
## m9 122.0 7.27 0.013
## m10 127.0 12.23 0.001
## m11 127.4 12.58 0.001
## m12 133.7 18.96 0.000
## Models ranked by AICc(x)
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning: glm.fit: algorithm did not converge
## Warning in glm.nb(y ~ log(maxvol) + log(mu.scalar) * (log(k.scalar) +
## I(log(k.scalar)^2)), : alternation limit reached
## Warning: glm.fit: fitted rates numerically 0 occurred
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning in glm.nb(y ~ log(maxvol) + (log(k.scalar) + I(log(k.scalar)^2))
## * : alternation limit reached
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 4.637 -0.6340
## m1 6.617 -0.9657 0.3999
## m2 4.816 -0.6647 -0.07571
## m3 6.862 -0.9355 -0.1256 -0.5627
## m4 8.644 -1.2550 -0.12780 -1.293
## m9 8.269 -1.2490 0.4293 -0.10200
## m10 10.510 -1.5870 1.5350 -0.12580 -1.144
## m11 9.161 -1.3280 -0.1070 -0.17620 -0.5725
## m12 18.050 -2.4300 0.2985 -0.30850 -9.4050 -6.367
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m1
## m2
## m3
## m4
## m9 0.2024
## m10 0.2371 -2.9160
## m11 0.2507
## m12 0.3960 -0.5263
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 family
## m0 NB(0.2198)
## m1 NB(0.2294)
## m2 NB(0.2199)
## m3 NB(0.2481)
## m4 NB(0.2265)
## m9 NB(0.2304)
## m10 NB(0.2356)
## m11 0.0585 NB(0.249)
## m12 0.1030 19.25 NB(0.3894)
## init.theta df logLik AICc delta weight
## m0 0.22 3 -55.168 117.3 0.00 0.526
## m1 0.229 4 -54.823 119.2 1.99 0.195
## m2 0.22 4 -55.163 119.9 2.67 0.139
## m3 0.248 5 -54.154 120.8 3.55 0.089
## m4 0.226 5 -54.935 122.4 5.11 0.041
## m9 0.23 6 -54.766 125.2 7.93 0.010
## m10 0.236 8 -53.917 130.7 13.43 0.001
## m11 0.249 8 -54.089 131.0 13.78 0.001
## m12 0.389 11 -50.234 137.1 19.88 0.000
## Abbreviations:
## family: NB(0.2198) = 'Negative Binomial(0.2198)',
## NB(0.2199) = 'Negative Binomial(0.2199)',
## NB(0.2265) = 'Negative Binomial(0.2265)',
## NB(0.2294) = 'Negative Binomial(0.2294)',
## NB(0.2304) = 'Negative Binomial(0.2304)',
## NB(0.2356) = 'Negative Binomial(0.2356)',
## NB(0.2481) = 'Negative Binomial(0.2481)',
## NB(0.249) = 'Negative Binomial(0.249)',
## NB(0.3894) = 'Negative Binomial(0.3894)'
## Models ranked by AICc(x)
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 4.637 -0.6340
## m1 6.640 -0.9696 0.4012
## m2 4.815 -0.6646 -0.07566
## m3 6.940 -0.9476 -0.1288 -0.5716
## m4 8.659 -1.2580 -0.12830 -1.295
## m9 8.328 -1.2590 0.4316 -0.10320
## m10 10.490 -1.5820 1.5630 -0.13040 -1.169
## m11 9.400 -1.3670 -0.1091 -0.18580 -0.5827
## m12 18.750 -2.5150 0.1782 -0.36720 -10.3000 -6.744
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m1
## m2
## m3
## m4
## m9 0.2043
## m10 0.2415 -2.9660
## m11 0.2602
## m12 0.4311 -0.2645
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik
## m0 2 -55.168
## m1 3 -54.829
## m2 3 -55.163
## m3 4 -54.199
## m4 4 -54.938
## m9 5 -54.773
## m10 7 -53.961
## m11 0.06286 7 -54.137
## m12 0.12240 21.06 10 -51.080
## AICc delta weight
## m0 114.8 0.00 0.494
## m1 116.6 1.80 0.201
## m2 117.2 2.47 0.144
## m3 118.0 3.22 0.099
## m4 119.5 4.70 0.047
## m9 122.0 7.27 0.013
## m10 127.0 12.23 0.001
## m11 127.4 12.58 0.001
## m12 133.7 18.96 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m4 13.550 -1.5800 -0.4537 -1.513
## m3 14.300 -1.7060 -0.41620 -0.4006
## m0 10.040 -1.1290
## m2 4.597 -0.3449 -0.4913
## m1 10.440 -1.1860 0.05285
## m10 12.920 -1.4830 0.33720 -0.4677 -1.679
## m11 9.433 -1.0040 -0.41680 -0.4616 -0.3921
## m9 4.926 -0.3912 0.04303 -0.4998
## m12 22.080 -2.7380 -0.44270 -0.3742 -0.7102 -2.368
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m4
## m3
## m0
## m2
## m1
## m10 -0.01972 -0.9697
## m11 -0.03799
## m9 -0.03060
## m12 -0.07142 0.0353
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 family
## m4 NB(2.3468)
## m3 NB(2.2894)
## m0 NB(1.7512)
## m2 NB(1.9095)
## m1 NB(1.7572)
## m10 NB(2.5213)
## m11 0.00613 NB(2.4977)
## m9 NB(1.9141)
## m12 -0.03572 0.9204 NB(4.1868)
## init.theta df logLik AICc delta weight
## m4 2.35 5 -92.951 198.4 0.00 0.421
## m3 2.29 5 -93.247 199.0 0.59 0.313
## m0 1.75 3 -97.086 201.1 2.69 0.110
## m2 1.91 4 -95.795 201.2 2.79 0.104
## m1 1.76 4 -97.037 203.7 5.27 0.030
## m10 2.52 8 -91.923 206.7 8.30 0.007
## m11 2.5 8 -91.938 206.7 8.33 0.007
## m9 1.91 6 -95.755 207.2 8.76 0.005
## m12 4.19 11 -85.700 208.1 9.67 0.003
## Abbreviations:
## family: NB(1.7512) = 'Negative Binomial(1.7512)',
## NB(1.7572) = 'Negative Binomial(1.7572)',
## NB(1.9095) = 'Negative Binomial(1.9095)',
## NB(1.9141) = 'Negative Binomial(1.9141)',
## NB(2.2894) = 'Negative Binomial(2.2894)',
## NB(2.3468) = 'Negative Binomial(2.3468)',
## NB(2.4977) = 'Negative Binomial(2.4977)',
## NB(2.5213) = 'Negative Binomial(2.5213)',
## NB(4.1868) = 'Negative Binomial(4.1868)'
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m4 13.550 -1.5800 -0.4537 -1.513
## m3 14.290 -1.7030 -0.41550 -0.4005
## m2 4.556 -0.3389 -0.4869
## m0 9.950 -1.1160
## m1 10.380 -1.1760 0.05244
## m10 12.920 -1.4830 0.33910 -0.4678 -1.680
## m11 9.486 -1.0120 -0.41900 -0.4626 -0.3928
## m9 4.902 -0.3875 0.04268 -0.4938
## m12 22.150 -2.7480 -0.46580 -0.3644 -0.7077 -2.334
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m4
## m3
## m2
## m0
## m1
## m10 -0.02020 -0.97540
## m11 -0.04104
## m9 -0.02666
## m12 -0.09697 0.04377
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik
## m4 4 -92.951
## m3 4 -93.250
## m2 3 -96.049
## m0 2 -97.617
## m1 3 -97.555
## m10 7 -91.948
## m11 0.00508 7 -91.957
## m9 5 -96.003
## m12 -0.05162 0.8962 10 -87.025
## AICc delta weight
## m4 195.5 0.00 0.467
## m3 196.1 0.60 0.346
## m2 199.0 3.52 0.080
## m0 199.7 4.18 0.058
## m1 202.0 6.53 0.018
## m10 203.0 7.49 0.011
## m11 203.0 7.50 0.011
## m9 204.5 9.00 0.005
## m12 205.6 10.13 0.003
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m4 13.550 -1.5800 -0.4537 -1.513
## m3 14.300 -1.7060 -0.41620 -0.4006
## m0 10.040 -1.1290
## m2 4.597 -0.3449 -0.4913
## m1 10.440 -1.1860 0.05285
## m10 12.920 -1.4830 0.33720 -0.4677 -1.679
## m11 9.433 -1.0040 -0.41680 -0.4616 -0.3921
## m9 4.926 -0.3912 0.04303 -0.4998
## m12 22.080 -2.7380 -0.44270 -0.3742 -0.7102 -2.368
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m4
## m3
## m0
## m2
## m1
## m10 -0.01972 -0.9697
## m11 -0.03799
## m9 -0.03060
## m12 -0.07142 0.0353
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 family
## m4 NB(2.3468)
## m3 NB(2.2894)
## m0 NB(1.7512)
## m2 NB(1.9095)
## m1 NB(1.7572)
## m10 NB(2.5213)
## m11 0.00613 NB(2.4977)
## m9 NB(1.9141)
## m12 -0.03572 0.9204 NB(4.1868)
## init.theta df logLik AICc delta weight
## m4 2.35 5 -92.951 198.4 0.00 0.421
## m3 2.29 5 -93.247 199.0 0.59 0.313
## m0 1.75 3 -97.086 201.1 2.69 0.110
## m2 1.91 4 -95.795 201.2 2.79 0.104
## m1 1.76 4 -97.037 203.7 5.27 0.030
## m10 2.52 8 -91.923 206.7 8.30 0.007
## m11 2.5 8 -91.938 206.7 8.33 0.007
## m9 1.91 6 -95.755 207.2 8.76 0.005
## m12 4.19 11 -85.700 208.1 9.67 0.003
## Abbreviations:
## family: NB(1.7512) = 'Negative Binomial(1.7512)',
## NB(1.7572) = 'Negative Binomial(1.7572)',
## NB(1.9095) = 'Negative Binomial(1.9095)',
## NB(1.9141) = 'Negative Binomial(1.9141)',
## NB(2.2894) = 'Negative Binomial(2.2894)',
## NB(2.3468) = 'Negative Binomial(2.3468)',
## NB(2.4977) = 'Negative Binomial(2.4977)',
## NB(2.5213) = 'Negative Binomial(2.5213)',
## NB(4.1868) = 'Negative Binomial(4.1868)'
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m4 13.550 -1.5800 -0.4537 -1.513
## m3 14.290 -1.7030 -0.41550 -0.4005
## m2 4.556 -0.3389 -0.4869
## m0 9.950 -1.1160
## m1 10.380 -1.1760 0.05244
## m10 12.920 -1.4830 0.33910 -0.4678 -1.680
## m11 9.486 -1.0120 -0.41900 -0.4626 -0.3928
## m9 4.902 -0.3875 0.04268 -0.4938
## m12 22.150 -2.7480 -0.46580 -0.3644 -0.7077 -2.334
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m4
## m3
## m2
## m0
## m1
## m10 -0.02020 -0.97540
## m11 -0.04104
## m9 -0.02666
## m12 -0.09697 0.04377
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik
## m4 4 -92.951
## m3 4 -93.250
## m2 3 -96.049
## m0 2 -97.617
## m1 3 -97.555
## m10 7 -91.948
## m11 0.00508 7 -91.957
## m9 5 -96.003
## m12 -0.05162 0.8962 10 -87.025
## AICc delta weight
## m4 195.5 0.00 0.467
## m3 196.1 0.60 0.346
## m2 199.0 3.52 0.080
## m0 199.7 4.18 0.058
## m1 202.0 6.53 0.018
## m10 203.0 7.49 0.011
## m11 203.0 7.50 0.011
## m9 204.5 9.00 0.005
## m12 205.6 10.13 0.003
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m1 -5.782 1.1110 -0.3277
## m0 -4.552 0.9108
## m2 -3.019 0.6233 -0.4040
## m3 -6.053 1.1450 -0.2142 0.09598
## m9 -4.243 0.8255 -0.3020 -0.3037
## m4 -3.019 0.6233 -0.4040 -0.0001068
## m11 -4.345 0.8293 -0.1999 -0.1972 0.08807
## m10 -3.581 0.7139 -0.1976 -0.3289 -0.1993000
## m12 -3.161 0.5679 0.2450 -0.2461 0.34770 0.7296000
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m1
## m0
## m2
## m3
## m9 0.09531
## m4
## m11 -0.03939
## m10 0.07704 -0.3477
## m12 -0.12810 -1.5930
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 family
## m1 NB(2.5244)
## m0 NB(1.9098)
## m2 NB(2.0721)
## m3 NB(2.5223)
## m9 NB(2.7642)
## m4 NB(2.0721)
## m11 -0.1236 NB(2.7194)
## m10 NB(2.7864)
## m12 -0.1607 -0.9718 NB(3.5069)
## init.theta df logLik AICc delta weight
## m1 2.52 4 -47.574 104.7 0.00 0.400
## m0 1.91 3 -49.230 105.4 0.64 0.291
## m2 2.07 4 -48.654 106.9 2.16 0.136
## m3 2.52 5 -47.431 107.4 2.61 0.108
## m9 2.76 6 -47.077 109.8 5.06 0.032
## m4 2.07 5 -48.654 109.8 5.06 0.032
## m11 2.72 8 -46.882 116.6 11.87 0.001
## m10 2.79 8 -46.975 116.8 12.06 0.001
## m12 3.51 11 -45.967 128.6 23.85 0.000
## Abbreviations:
## family: NB(1.9098) = 'Negative Binomial(1.9098)',
## NB(2.0721) = 'Negative Binomial(2.0721)',
## NB(2.5223) = 'Negative Binomial(2.5223)',
## NB(2.5244) = 'Negative Binomial(2.5244)',
## NB(2.7194) = 'Negative Binomial(2.7194)',
## NB(2.7642) = 'Negative Binomial(2.7642)',
## NB(2.7864) = 'Negative Binomial(2.7864)',
## NB(3.5069) = 'Negative Binomial(3.5069)'
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m1 -5.782 1.1110 -0.3277
## m0 -4.521 0.9051
## m2 -2.995 0.6189 -0.4037
## m3 -6.053 1.1450 -0.2142 0.09599
## m9 -4.249 0.8266 -0.3017 -0.3049
## m4 -3.002 0.6198 -0.4034 0.004491
## m11 -4.352 0.8301 -0.1985 -0.1937 0.08981
## m10 -3.576 0.7131 -0.1966 -0.3305 -0.202500
## m12 -3.076 0.5547 0.2358 -0.2415 0.33870 0.683200
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m1
## m0
## m2
## m3
## m9 0.09354
## m4
## m11 -0.04468
## m10 0.07547 -0.3505
## m12 -0.13730 -1.5440
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik
## m1 3 -47.574
## m0 2 -49.335
## m2 3 -48.703
## m3 4 -47.431
## m9 5 -47.086
## m4 4 -48.703
## m11 -0.1284 7 -46.888
## m10 7 -46.986
## m12 -0.1697 -0.9305 10 -46.066
## AICc delta weight
## m1 102.1 0.00 0.414
## m0 103.1 1.04 0.246
## m2 104.3 2.26 0.134
## m3 104.5 2.39 0.125
## m9 106.7 4.60 0.042
## m4 107.0 4.94 0.035
## m11 112.9 10.80 0.002
## m10 113.1 10.99 0.002
## m12 123.7 21.64 0.000
## Models ranked by AICc(x)
## Warning in glm.nb(y ~ log(maxvol) + cv.depth, data = dataset): alternation
## limit reached
## Warning in glm.nb(y ~ log(maxvol) + cv.depth, data = dataset): glm.fit:
## algorithm did not converge
## Warning in glm.nb(y ~ log(maxvol) + cv.depth, data = dataset): glm.fit:
## algorithm did not converge
## Warning in glm.nb(y ~ log(maxvol) + cv.depth, data = dataset): glm.fit:
## algorithm did not converge
## Warning in glm.nb(y ~ log(maxvol) + cv.depth, data = dataset): glm.fit:
## algorithm did not converge
## Warning in glm.nb(y ~ log(maxvol) + cv.depth, data = dataset): glm.fit:
## algorithm did not converge
## Warning in glm.nb(y ~ log(maxvol) + mean.depth, data = dataset):
## alternation limit reached
## Model selection table
## (Int) log(mxv) cv.dpt prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m4 17.640 -3.1170 0.03578
## m0 4.637 -0.6340
## m2 13.710 -2.3960 6.04
## m1 13.470 -2.0540 -0.01021
## m3 9.698 -1.4680 -1.213
## m5 6.845 -0.9999 -0.01188
## m6 7.023 -1.0230
## lst_wet family init.theta df logLik AICc delta weight
## m4 NB(0.2912) 0.291 4 -53.209 116.0 0.00 0.405
## m0 NB(0.2198) 0.22 3 -55.168 117.3 1.24 0.218
## m2 NB(0.2414) 0.241 4 -54.411 118.4 2.40 0.122
## m1 NB(0.2271) 0.227 4 -54.874 119.3 3.33 0.077
## m3 NB(0.2219) 0.222 4 -55.075 119.7 3.73 0.063
## m5 NB(0.2205) 0.221 4 -55.136 119.9 3.85 0.059
## m6 -0.008155 NB(0.2201) 0.22 4 -55.148 119.9 3.88 0.058
## Abbreviations:
## family: NB(0.2198) = 'Negative Binomial(0.2198)',
## NB(0.2201) = 'Negative Binomial(0.2201)',
## NB(0.2205) = 'Negative Binomial(0.2205)',
## NB(0.2219) = 'Negative Binomial(0.2219)',
## NB(0.2271) = 'Negative Binomial(0.2271)',
## NB(0.2414) = 'Negative Binomial(0.2414)',
## NB(0.2912) = 'Negative Binomial(0.2912)'
## Models ranked by AICc(x)
## Warning: glm.fit: algorithm did not converge
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m4 17.700 -3.1240 0.03526
## m2 13.430 -2.3440 5.928
## m1 12.980 -0.009794 -1.9740
## m3 9.330 -1.4060 -1.148
## m5 6.704 -0.9762 -0.01142
## m6 6.764 -0.9804
## lst_wet df logLik AICc delta weight
## m4 3 -53.210 113.3 0.00 0.565
## m2 3 -54.523 116.0 2.63 0.152
## m1 3 -55.078 117.1 3.74 0.087
## m3 3 -55.320 117.6 4.22 0.069
## m5 3 -55.393 117.7 4.37 0.064
## m6 -0.007461 3 -55.409 117.7 4.40 0.063
## Models ranked by AICc(x)
## Warning in glm.nb(y ~ log(maxvol) + cv.depth, data = dataset): alternation
## limit reached
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning in glm.nb(y ~ log(maxvol) + mean.depth, data = dataset):
## alternation limit reached
## Model selection table
## (Int) log(mxv) cv.dpt prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m4 17.640 -3.1170 0.03578
## m0 4.637 -0.6340
## m2 13.710 -2.3960 6.04
## m1 13.470 -2.0540 -0.01021
## m3 9.698 -1.4680 -1.213
## m5 6.845 -0.9999 -0.01188
## m6 7.023 -1.0230
## lst_wet family init.theta df logLik AICc delta weight
## m4 NB(0.2912) 0.291 4 -53.209 116.0 0.00 0.405
## m0 NB(0.2198) 0.22 3 -55.168 117.3 1.24 0.218
## m2 NB(0.2414) 0.241 4 -54.411 118.4 2.40 0.122
## m1 NB(0.2271) 0.227 4 -54.874 119.3 3.33 0.077
## m3 NB(0.2219) 0.222 4 -55.075 119.7 3.73 0.063
## m5 NB(0.2205) 0.221 4 -55.136 119.9 3.85 0.059
## m6 -0.008155 NB(0.2201) 0.22 4 -55.148 119.9 3.88 0.058
## Abbreviations:
## family: NB(0.2198) = 'Negative Binomial(0.2198)',
## NB(0.2201) = 'Negative Binomial(0.2201)',
## NB(0.2205) = 'Negative Binomial(0.2205)',
## NB(0.2219) = 'Negative Binomial(0.2219)',
## NB(0.2271) = 'Negative Binomial(0.2271)',
## NB(0.2414) = 'Negative Binomial(0.2414)',
## NB(0.2912) = 'Negative Binomial(0.2912)'
## Models ranked by AICc(x)
## Warning: glm.fit: algorithm did not converge
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m4 17.700 -3.1240 0.03526
## m2 13.430 -2.3440 5.928
## m1 12.980 -0.009794 -1.9740
## m3 9.330 -1.4060 -1.148
## m5 6.704 -0.9762 -0.01142
## m6 6.764 -0.9804
## lst_wet df logLik AICc delta weight
## m4 3 -53.210 113.3 0.00 0.565
## m2 3 -54.523 116.0 2.63 0.152
## m1 3 -55.078 117.1 3.74 0.087
## m3 3 -55.320 117.6 4.22 0.069
## m5 3 -55.393 117.7 4.37 0.064
## m6 -0.007461 3 -55.409 117.7 4.40 0.063
## Models ranked by AICc(x)
## Warning in glm.nb(y ~ log(maxvol) + cv.depth, data = dataset): alternation
## limit reached
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning in glm.nb(y ~ log(maxvol) + mean.depth, data = dataset):
## alternation limit reached
## Model selection table
## (Int) log(mxv) cv.dpt prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m4 17.640 -3.1170 0.03578
## m0 4.637 -0.6340
## m2 13.710 -2.3960 6.04
## m1 13.470 -2.0540 -0.01021
## m3 9.698 -1.4680 -1.213
## m5 6.845 -0.9999 -0.01188
## m6 7.023 -1.0230
## lst_wet family init.theta df logLik AICc delta weight
## m4 NB(0.2912) 0.291 4 -53.209 116.0 0.00 0.405
## m0 NB(0.2198) 0.22 3 -55.168 117.3 1.24 0.218
## m2 NB(0.2414) 0.241 4 -54.411 118.4 2.40 0.122
## m1 NB(0.2271) 0.227 4 -54.874 119.3 3.33 0.077
## m3 NB(0.2219) 0.222 4 -55.075 119.7 3.73 0.063
## m5 NB(0.2205) 0.221 4 -55.136 119.9 3.85 0.059
## m6 -0.008155 NB(0.2201) 0.22 4 -55.148 119.9 3.88 0.058
## Abbreviations:
## family: NB(0.2198) = 'Negative Binomial(0.2198)',
## NB(0.2201) = 'Negative Binomial(0.2201)',
## NB(0.2205) = 'Negative Binomial(0.2205)',
## NB(0.2219) = 'Negative Binomial(0.2219)',
## NB(0.2271) = 'Negative Binomial(0.2271)',
## NB(0.2414) = 'Negative Binomial(0.2414)',
## NB(0.2912) = 'Negative Binomial(0.2912)'
## Models ranked by AICc(x)
## Warning: glm.fit: algorithm did not converge
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m4 17.700 -3.1240 0.03526
## m2 13.430 -2.3440 5.928
## m1 12.980 -0.009794 -1.9740
## m3 9.330 -1.4060 -1.148
## m5 6.704 -0.9762 -0.01142
## m6 6.764 -0.9804
## lst_wet df logLik AICc delta weight
## m4 3 -53.210 113.3 0.00 0.565
## m2 3 -54.523 116.0 2.63 0.152
## m1 3 -55.078 117.1 3.74 0.087
## m3 3 -55.320 117.6 4.22 0.069
## m5 3 -55.393 117.7 4.37 0.064
## m6 -0.007461 3 -55.409 117.7 4.40 0.063
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) cv.dpt prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m0 10.040 -1.129
## m2 10.020 -1.094 -3.809
## m6 11.660 -1.401
## m3 9.583 -1.059 -0.7445
## m4 10.350 -1.124 -0.005511
## m5 9.775 -1.087 -0.01572
## m1 10.130 -1.144 0.0003387
## lst_wet family init.theta df logLik AICc delta weight
## m0 NB(1.7512) 1.75 3 -97.086 201.1 0.00 0.332
## m2 NB(1.8009) 1.8 4 -96.660 202.9 1.83 0.133
## m6 0.008153 NB(1.7931) 1.79 4 -96.687 203.0 1.88 0.130
## m3 NB(1.7805) 1.78 4 -96.848 203.3 2.20 0.110
## m4 NB(1.7716) 1.77 4 -96.902 203.4 2.31 0.105
## m5 NB(1.7721) 1.77 4 -96.916 203.4 2.34 0.103
## m1 NB(1.7514) 1.75 4 -97.083 203.8 2.67 0.087
## Abbreviations:
## family: NB(1.7512) = 'Negative Binomial(1.7512)',
## NB(1.7514) = 'Negative Binomial(1.7514)',
## NB(1.7716) = 'Negative Binomial(1.7716)',
## NB(1.7721) = 'Negative Binomial(1.7721)',
## NB(1.7805) = 'Negative Binomial(1.7805)',
## NB(1.7931) = 'Negative Binomial(1.7931)',
## NB(1.8009) = 'Negative Binomial(1.8009)'
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m2 10.040 -1.095 -3.812
## m6 11.680 -1.404
## m3 9.588 -1.060 -0.7443
## m4 10.350 -1.124 -0.005516
## m5 9.778 -1.088 -0.01572
## m1 10.130 0.000339 -1.144
## lst_wet df logLik AICc delta weight
## m2 3 -96.665 200.3 0.00 0.199
## m6 0.008173 3 -96.691 200.3 0.05 0.194
## m3 3 -96.850 200.6 0.37 0.165
## m4 3 -96.903 200.7 0.48 0.157
## m5 3 -96.917 200.8 0.50 0.155
## m1 3 -97.083 201.1 0.84 0.131
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) cv.dpt prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m0 10.040 -1.129
## m2 10.020 -1.094 -3.809
## m6 11.660 -1.401
## m3 9.583 -1.059 -0.7445
## m4 10.350 -1.124 -0.005511
## m5 9.775 -1.087 -0.01572
## m1 10.130 -1.144 0.0003387
## lst_wet family init.theta df logLik AICc delta weight
## m0 NB(1.7512) 1.75 3 -97.086 201.1 0.00 0.332
## m2 NB(1.8009) 1.8 4 -96.660 202.9 1.83 0.133
## m6 0.008153 NB(1.7931) 1.79 4 -96.687 203.0 1.88 0.130
## m3 NB(1.7805) 1.78 4 -96.848 203.3 2.20 0.110
## m4 NB(1.7716) 1.77 4 -96.902 203.4 2.31 0.105
## m5 NB(1.7721) 1.77 4 -96.916 203.4 2.34 0.103
## m1 NB(1.7514) 1.75 4 -97.083 203.8 2.67 0.087
## Abbreviations:
## family: NB(1.7512) = 'Negative Binomial(1.7512)',
## NB(1.7514) = 'Negative Binomial(1.7514)',
## NB(1.7716) = 'Negative Binomial(1.7716)',
## NB(1.7721) = 'Negative Binomial(1.7721)',
## NB(1.7805) = 'Negative Binomial(1.7805)',
## NB(1.7931) = 'Negative Binomial(1.7931)',
## NB(1.8009) = 'Negative Binomial(1.8009)'
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m2 10.040 -1.095 -3.812
## m6 11.680 -1.404
## m3 9.588 -1.060 -0.7443
## m4 10.350 -1.124 -0.005516
## m5 9.778 -1.088 -0.01572
## m1 10.130 0.000339 -1.144
## lst_wet df logLik AICc delta weight
## m2 3 -96.665 200.3 0.00 0.199
## m6 0.008173 3 -96.691 200.3 0.05 0.194
## m3 3 -96.850 200.6 0.37 0.165
## m4 3 -96.903 200.7 0.48 0.157
## m5 3 -96.917 200.8 0.50 0.155
## m1 3 -97.083 201.1 0.84 0.131
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) cv.dpt prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m0 -4.552 0.9108
## m5 -3.396 0.7099 -0.07504
## m4 -3.108 0.5484 0.01042
## m1 -2.884 0.6414 -0.00838
## m6 -3.879 0.7972
## m3 -4.051 0.8231 -1.33
## m2 -4.751 0.9600 -0.2806
## lst_wet family init.theta df logLik AICc delta weight
## m0 NB(1.9098) 1.91 3 -49.230 105.4 0.00 0.365
## m5 NB(1.9648) 1.96 4 -49.012 107.6 2.24 0.119
## m4 NB(1.9524) 1.95 4 -49.095 107.8 2.41 0.110
## m1 NB(1.9513) 1.95 4 -49.121 107.8 2.46 0.107
## m6 -0.0128 NB(1.9345) 1.93 4 -49.156 107.9 2.53 0.103
## m3 NB(1.929) 1.93 4 -49.195 108.0 2.61 0.099
## m2 NB(1.9113) 1.91 4 -49.209 108.0 2.63 0.098
## Abbreviations:
## family: NB(1.9098) = 'Negative Binomial(1.9098)',
## NB(1.9113) = 'Negative Binomial(1.9113)',
## NB(1.929) = 'Negative Binomial(1.929)',
## NB(1.9345) = 'Negative Binomial(1.9345)',
## NB(1.9513) = 'Negative Binomial(1.9513)',
## NB(1.9524) = 'Negative Binomial(1.9524)',
## NB(1.9648) = 'Negative Binomial(1.9648)'
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m5 -3.394 0.7096 -0.07517
## m4 -3.108 0.5483 0.01043
## m1 -2.885 -0.008374 0.6416
## m6 -3.880 0.7973
## m3 -4.053 0.8234 -1.327
## m2 -4.751 0.9601 -0.2807
## lst_wet df logLik AICc delta weight
## m5 3 -49.013 104.9 0.00 0.187
## m4 3 -49.096 105.1 0.17 0.172
## m1 3 -49.122 105.2 0.22 0.168
## m6 -0.0128 3 -49.156 105.2 0.29 0.162
## m3 3 -49.195 105.3 0.37 0.156
## m2 3 -49.209 105.3 0.39 0.154
## Models ranked by AICc(x)
## Error in nrow(concord.out): object 'concord.out' not found
sites<-c("puertorico", "macae", "frenchguiana", "costarica", "colombia", "argentina")
concord.out5<-concord.magic(sites, "gatherer_bio", no67185data, 100, 2, "nb")#a handful of models fail, not too bad
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m1 4.4080 -0.06618 -0.5277
## m0 -0.6308 0.84300
## m3 4.2310 -0.03894 -0.5065 0.01961
## m2 -0.6223 0.84160 -0.02149
## m9 6.8090 -0.48840 -0.5640 -0.60720
## m4 -0.2421 0.75080 -0.02236 0.4543
## m10 3.3780 0.11770 -0.1474 -0.53300 -0.4717
## m11 8.2820 -0.72000 -0.6828 -0.82900 -0.14030
## m12 2.0370 0.30220 0.1155 -0.68600 0.25990 0.5661
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m1
## m0
## m3
## m2
## m9 -0.5217
## m4
## m10 -0.4365 -1.300
## m11 -0.2766
## m12 -0.3961 -2.302
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 family
## m1 NB(0.487)
## m0 NB(0.4279)
## m3 NB(0.4871)
## m2 NB(0.4279)
## m9 NB(0.5157)
## m4 NB(0.4297)
## m10 NB(0.5388)
## m11 0.2827 NB(0.5249)
## m12 0.1814 -1.133 NB(0.5618)
## init.theta df logLik AICc delta weight
## m1 0.487 4 -150.366 310.3 0.00 0.484
## m0 0.428 3 -152.337 311.6 1.26 0.257
## m3 0.487 5 -150.363 313.2 2.89 0.114
## m2 0.428 4 -152.336 314.3 3.94 0.068
## m9 0.516 6 -149.514 314.7 4.35 0.055
## m4 0.43 5 -152.272 317.0 6.71 0.017
## m10 0.539 8 -148.880 320.6 10.29 0.003
## m11 0.525 8 -149.260 321.4 11.05 0.002
## m12 0.562 11 -148.286 333.2 22.91 0.000
## Abbreviations:
## family: NB(0.4279) = 'Negative Binomial(0.4279)',
## NB(0.4297) = 'Negative Binomial(0.4297)',
## NB(0.487) = 'Negative Binomial(0.487)',
## NB(0.4871) = 'Negative Binomial(0.4871)',
## NB(0.5157) = 'Negative Binomial(0.5157)',
## NB(0.5249) = 'Negative Binomial(0.5249)',
## NB(0.5388) = 'Negative Binomial(0.5388)',
## NB(0.5618) = 'Negative Binomial(0.5618)'
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m1 4.4080 -0.06612 -0.5277
## m0 -0.6303 0.84290
## m3 4.2300 -0.03883 -0.5065 0.0196
## m9 6.8140 -0.48930 -0.5641 -0.60750
## m2 -0.6218 0.84150 -0.02147
## m4 -0.2419 0.75070 -0.02234 0.4544
## m10 3.3820 0.11700 -0.1474 -0.53320 -0.4719
## m11 8.2890 -0.72130 -0.6831 -0.82930 -0.1405
## m12 2.0450 0.30090 0.1151 -0.68590 0.2597 0.5665
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m1
## m0
## m3
## m9 -0.5219
## m2
## m4
## m10 -0.4367 -1.300
## m11 -0.2772
## m12 -0.3972 -2.303
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik
## m1 3 -150.366
## m0 2 -152.470
## m3 4 -150.363
## m9 5 -149.537
## m2 3 -152.469
## m4 4 -152.397
## m10 7 -148.951
## m11 0.2825 7 -149.299
## m12 0.1809 -1.133 10 -148.424
## AICc delta weight
## m1 307.7 0.00 0.501
## m0 309.4 1.73 0.211
## m3 310.3 2.67 0.132
## m9 311.6 3.92 0.071
## m2 311.9 4.21 0.061
## m4 314.4 6.74 0.017
## m10 317.0 9.34 0.005
## m11 317.7 10.04 0.003
## m12 328.4 20.77 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m1 4.4080 -0.06618 -0.5277
## m0 -0.6308 0.84300
## m3 4.2310 -0.03894 -0.5065 0.01961
## m2 -0.6223 0.84160 -0.02149
## m9 6.8090 -0.48840 -0.5640 -0.60720
## m4 -0.2421 0.75080 -0.02236 0.4543
## m10 3.3780 0.11770 -0.1474 -0.53300 -0.4717
## m11 8.2820 -0.72000 -0.6828 -0.82900 -0.14030
## m12 2.0370 0.30220 0.1155 -0.68600 0.25990 0.5661
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m1
## m0
## m3
## m2
## m9 -0.5217
## m4
## m10 -0.4365 -1.300
## m11 -0.2766
## m12 -0.3961 -2.302
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 family
## m1 NB(0.487)
## m0 NB(0.4279)
## m3 NB(0.4871)
## m2 NB(0.4279)
## m9 NB(0.5157)
## m4 NB(0.4297)
## m10 NB(0.5388)
## m11 0.2827 NB(0.5249)
## m12 0.1814 -1.133 NB(0.5618)
## init.theta df logLik AICc delta weight
## m1 0.487 4 -150.366 310.3 0.00 0.484
## m0 0.428 3 -152.337 311.6 1.26 0.257
## m3 0.487 5 -150.363 313.2 2.89 0.114
## m2 0.428 4 -152.336 314.3 3.94 0.068
## m9 0.516 6 -149.514 314.7 4.35 0.055
## m4 0.43 5 -152.272 317.0 6.71 0.017
## m10 0.539 8 -148.880 320.6 10.29 0.003
## m11 0.525 8 -149.260 321.4 11.05 0.002
## m12 0.562 11 -148.286 333.2 22.91 0.000
## Abbreviations:
## family: NB(0.4279) = 'Negative Binomial(0.4279)',
## NB(0.4297) = 'Negative Binomial(0.4297)',
## NB(0.487) = 'Negative Binomial(0.487)',
## NB(0.4871) = 'Negative Binomial(0.4871)',
## NB(0.5157) = 'Negative Binomial(0.5157)',
## NB(0.5249) = 'Negative Binomial(0.5249)',
## NB(0.5388) = 'Negative Binomial(0.5388)',
## NB(0.5618) = 'Negative Binomial(0.5618)'
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m1 4.4080 -0.06612 -0.5277
## m0 -0.6303 0.84290
## m3 4.2300 -0.03883 -0.5065 0.0196
## m9 6.8140 -0.48930 -0.5641 -0.60750
## m2 -0.6218 0.84150 -0.02147
## m4 -0.2419 0.75070 -0.02234 0.4544
## m10 3.3820 0.11700 -0.1474 -0.53320 -0.4719
## m11 8.2890 -0.72130 -0.6831 -0.82930 -0.1405
## m12 2.0450 0.30090 0.1151 -0.68590 0.2597 0.5665
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m1
## m0
## m3
## m9 -0.5219
## m2
## m4
## m10 -0.4367 -1.300
## m11 -0.2772
## m12 -0.3972 -2.303
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik
## m1 3 -150.366
## m0 2 -152.470
## m3 4 -150.363
## m9 5 -149.537
## m2 3 -152.469
## m4 4 -152.397
## m10 7 -148.951
## m11 0.2825 7 -149.299
## m12 0.1809 -1.133 10 -148.424
## AICc delta weight
## m1 307.7 0.00 0.501
## m0 309.4 1.73 0.211
## m3 310.3 2.67 0.132
## m9 311.6 3.92 0.071
## m2 311.9 4.21 0.061
## m4 314.4 6.74 0.017
## m10 317.0 9.34 0.005
## m11 317.7 10.04 0.003
## m12 328.4 20.77 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m1 4.4080 -0.06618 -0.5277
## m0 -0.6308 0.84300
## m3 4.2310 -0.03894 -0.5065 0.01961
## m2 -0.6223 0.84160 -0.02149
## m9 6.8090 -0.48840 -0.5640 -0.60720
## m4 -0.2421 0.75080 -0.02236 0.4543
## m10 3.3780 0.11770 -0.1474 -0.53300 -0.4717
## m11 8.2820 -0.72000 -0.6828 -0.82900 -0.14030
## m12 2.0370 0.30220 0.1155 -0.68600 0.25990 0.5661
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m1
## m0
## m3
## m2
## m9 -0.5217
## m4
## m10 -0.4365 -1.300
## m11 -0.2766
## m12 -0.3961 -2.302
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 family
## m1 NB(0.487)
## m0 NB(0.4279)
## m3 NB(0.4871)
## m2 NB(0.4279)
## m9 NB(0.5157)
## m4 NB(0.4297)
## m10 NB(0.5388)
## m11 0.2827 NB(0.5249)
## m12 0.1814 -1.133 NB(0.5618)
## init.theta df logLik AICc delta weight
## m1 0.487 4 -150.366 310.3 0.00 0.484
## m0 0.428 3 -152.337 311.6 1.26 0.257
## m3 0.487 5 -150.363 313.2 2.89 0.114
## m2 0.428 4 -152.336 314.3 3.94 0.068
## m9 0.516 6 -149.514 314.7 4.35 0.055
## m4 0.43 5 -152.272 317.0 6.71 0.017
## m10 0.539 8 -148.880 320.6 10.29 0.003
## m11 0.525 8 -149.260 321.4 11.05 0.002
## m12 0.562 11 -148.286 333.2 22.91 0.000
## Abbreviations:
## family: NB(0.4279) = 'Negative Binomial(0.4279)',
## NB(0.4297) = 'Negative Binomial(0.4297)',
## NB(0.487) = 'Negative Binomial(0.487)',
## NB(0.4871) = 'Negative Binomial(0.4871)',
## NB(0.5157) = 'Negative Binomial(0.5157)',
## NB(0.5249) = 'Negative Binomial(0.5249)',
## NB(0.5388) = 'Negative Binomial(0.5388)',
## NB(0.5618) = 'Negative Binomial(0.5618)'
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m1 4.4080 -0.06612 -0.5277
## m0 -0.6303 0.84290
## m3 4.2300 -0.03883 -0.5065 0.0196
## m9 6.8140 -0.48930 -0.5641 -0.60750
## m2 -0.6218 0.84150 -0.02147
## m4 -0.2419 0.75070 -0.02234 0.4544
## m10 3.3820 0.11700 -0.1474 -0.53320 -0.4719
## m11 8.2890 -0.72130 -0.6831 -0.82930 -0.1405
## m12 2.0450 0.30090 0.1151 -0.68590 0.2597 0.5665
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m1
## m0
## m3
## m9 -0.5219
## m2
## m4
## m10 -0.4367 -1.300
## m11 -0.2772
## m12 -0.3972 -2.303
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik
## m1 3 -150.366
## m0 2 -152.470
## m3 4 -150.363
## m9 5 -149.537
## m2 3 -152.469
## m4 4 -152.397
## m10 7 -148.951
## m11 0.2825 7 -149.299
## m12 0.1809 -1.133 10 -148.424
## AICc delta weight
## m1 307.7 0.00 0.501
## m0 309.4 1.73 0.211
## m3 310.3 2.67 0.132
## m9 311.6 3.92 0.071
## m2 311.9 4.21 0.061
## m4 314.4 6.74 0.017
## m10 317.0 9.34 0.005
## m11 317.7 10.04 0.003
## m12 328.4 20.77 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m1 4.4080 -0.06618 -0.5277
## m0 -0.6308 0.84300
## m3 4.2310 -0.03894 -0.5065 0.01961
## m2 -0.6223 0.84160 -0.02149
## m9 6.8090 -0.48840 -0.5640 -0.60720
## m4 -0.2421 0.75080 -0.02236 0.4543
## m10 3.3780 0.11770 -0.1474 -0.53300 -0.4717
## m11 8.2820 -0.72000 -0.6828 -0.82900 -0.14030
## m12 2.0370 0.30220 0.1155 -0.68600 0.25990 0.5661
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m1
## m0
## m3
## m2
## m9 -0.5217
## m4
## m10 -0.4365 -1.300
## m11 -0.2766
## m12 -0.3961 -2.302
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 family
## m1 NB(0.487)
## m0 NB(0.4279)
## m3 NB(0.4871)
## m2 NB(0.4279)
## m9 NB(0.5157)
## m4 NB(0.4297)
## m10 NB(0.5388)
## m11 0.2827 NB(0.5249)
## m12 0.1814 -1.133 NB(0.5618)
## init.theta df logLik AICc delta weight
## m1 0.487 4 -150.366 310.3 0.00 0.484
## m0 0.428 3 -152.337 311.6 1.26 0.257
## m3 0.487 5 -150.363 313.2 2.89 0.114
## m2 0.428 4 -152.336 314.3 3.94 0.068
## m9 0.516 6 -149.514 314.7 4.35 0.055
## m4 0.43 5 -152.272 317.0 6.71 0.017
## m10 0.539 8 -148.880 320.6 10.29 0.003
## m11 0.525 8 -149.260 321.4 11.05 0.002
## m12 0.562 11 -148.286 333.2 22.91 0.000
## Abbreviations:
## family: NB(0.4279) = 'Negative Binomial(0.4279)',
## NB(0.4297) = 'Negative Binomial(0.4297)',
## NB(0.487) = 'Negative Binomial(0.487)',
## NB(0.4871) = 'Negative Binomial(0.4871)',
## NB(0.5157) = 'Negative Binomial(0.5157)',
## NB(0.5249) = 'Negative Binomial(0.5249)',
## NB(0.5388) = 'Negative Binomial(0.5388)',
## NB(0.5618) = 'Negative Binomial(0.5618)'
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m1 4.4080 -0.06612 -0.5277
## m0 -0.6303 0.84290
## m3 4.2300 -0.03883 -0.5065 0.0196
## m9 6.8140 -0.48930 -0.5641 -0.60750
## m2 -0.6218 0.84150 -0.02147
## m4 -0.2419 0.75070 -0.02234 0.4544
## m10 3.3820 0.11700 -0.1474 -0.53320 -0.4719
## m11 8.2890 -0.72130 -0.6831 -0.82930 -0.1405
## m12 2.0450 0.30090 0.1151 -0.68590 0.2597 0.5665
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m1
## m0
## m3
## m9 -0.5219
## m2
## m4
## m10 -0.4367 -1.300
## m11 -0.2772
## m12 -0.3972 -2.303
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik
## m1 3 -150.366
## m0 2 -152.470
## m3 4 -150.363
## m9 5 -149.537
## m2 3 -152.469
## m4 4 -152.397
## m10 7 -148.951
## m11 0.2825 7 -149.299
## m12 0.1809 -1.133 10 -148.424
## AICc delta weight
## m1 307.7 0.00 0.501
## m0 309.4 1.73 0.211
## m3 310.3 2.67 0.132
## m9 311.6 3.92 0.071
## m2 311.9 4.21 0.061
## m4 314.4 6.74 0.017
## m10 317.0 9.34 0.005
## m11 317.7 10.04 0.003
## m12 328.4 20.77 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m1 4.4080 -0.06618 -0.5277
## m0 -0.6308 0.84300
## m3 4.2310 -0.03894 -0.5065 0.01961
## m2 -0.6223 0.84160 -0.02149
## m9 6.8090 -0.48840 -0.5640 -0.60720
## m4 -0.2421 0.75080 -0.02236 0.4543
## m10 3.3780 0.11770 -0.1474 -0.53300 -0.4717
## m11 8.2820 -0.72000 -0.6828 -0.82900 -0.14030
## m12 2.0370 0.30220 0.1155 -0.68600 0.25990 0.5661
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m1
## m0
## m3
## m2
## m9 -0.5217
## m4
## m10 -0.4365 -1.300
## m11 -0.2766
## m12 -0.3961 -2.302
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 family
## m1 NB(0.487)
## m0 NB(0.4279)
## m3 NB(0.4871)
## m2 NB(0.4279)
## m9 NB(0.5157)
## m4 NB(0.4297)
## m10 NB(0.5388)
## m11 0.2827 NB(0.5249)
## m12 0.1814 -1.133 NB(0.5618)
## init.theta df logLik AICc delta weight
## m1 0.487 4 -150.366 310.3 0.00 0.484
## m0 0.428 3 -152.337 311.6 1.26 0.257
## m3 0.487 5 -150.363 313.2 2.89 0.114
## m2 0.428 4 -152.336 314.3 3.94 0.068
## m9 0.516 6 -149.514 314.7 4.35 0.055
## m4 0.43 5 -152.272 317.0 6.71 0.017
## m10 0.539 8 -148.880 320.6 10.29 0.003
## m11 0.525 8 -149.260 321.4 11.05 0.002
## m12 0.562 11 -148.286 333.2 22.91 0.000
## Abbreviations:
## family: NB(0.4279) = 'Negative Binomial(0.4279)',
## NB(0.4297) = 'Negative Binomial(0.4297)',
## NB(0.487) = 'Negative Binomial(0.487)',
## NB(0.4871) = 'Negative Binomial(0.4871)',
## NB(0.5157) = 'Negative Binomial(0.5157)',
## NB(0.5249) = 'Negative Binomial(0.5249)',
## NB(0.5388) = 'Negative Binomial(0.5388)',
## NB(0.5618) = 'Negative Binomial(0.5618)'
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m1 4.4080 -0.06612 -0.5277
## m0 -0.6303 0.84290
## m3 4.2300 -0.03883 -0.5065 0.0196
## m9 6.8140 -0.48930 -0.5641 -0.60750
## m2 -0.6218 0.84150 -0.02147
## m4 -0.2419 0.75070 -0.02234 0.4544
## m10 3.3820 0.11700 -0.1474 -0.53320 -0.4719
## m11 8.2890 -0.72130 -0.6831 -0.82930 -0.1405
## m12 2.0450 0.30090 0.1151 -0.68590 0.2597 0.5665
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m1
## m0
## m3
## m9 -0.5219
## m2
## m4
## m10 -0.4367 -1.300
## m11 -0.2772
## m12 -0.3972 -2.303
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik
## m1 3 -150.366
## m0 2 -152.470
## m3 4 -150.363
## m9 5 -149.537
## m2 3 -152.469
## m4 4 -152.397
## m10 7 -148.951
## m11 0.2825 7 -149.299
## m12 0.1809 -1.133 10 -148.424
## AICc delta weight
## m1 307.7 0.00 0.501
## m0 309.4 1.73 0.211
## m3 310.3 2.67 0.132
## m9 311.6 3.92 0.071
## m2 311.9 4.21 0.061
## m4 314.4 6.74 0.017
## m10 317.0 9.34 0.005
## m11 317.7 10.04 0.003
## m12 328.4 20.77 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 5.8110 0.07013
## m3 7.0880 -0.13860 0.26160 0.1758
## m2 3.8200 0.35790 -0.1814
## m1 7.4260 -0.16180 0.07318
## m4 3.5710 0.39090 -0.1830 0.06464
## m9 5.4950 0.11720 0.06617 -0.1697
## m11 4.6360 0.21300 0.25510 -0.3196 0.1884
## m10 5.4240 0.12670 0.16280 -0.1686 0.00150
## m12 -0.3017 0.89640 0.55320 -0.3596 0.3537 0.63650
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m3
## m2
## m1
## m4
## m9 7.476e-05
## m11 8.322e-02
## m10 5.593e-03 -0.3127
## m12 1.002e-01 -0.9594
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 family
## m0 NB(3.322)
## m3 NB(3.8043)
## m2 NB(3.4236)
## m1 NB(3.3874)
## m4 NB(3.4258)
## m9 NB(3.4799)
## m11 0.1245 NB(4.0552)
## m10 NB(3.5613)
## m12 0.1372 -0.5063 NB(4.4616)
## init.theta df logLik AICc delta weight
## m0 3.32 3 -210.242 427.4 0.00 0.403
## m3 3.8 5 -208.042 428.6 1.18 0.224
## m2 3.42 4 -209.751 429.1 1.69 0.173
## m1 3.39 4 -209.924 429.4 2.04 0.145
## m4 3.43 5 -209.741 432.0 4.57 0.041
## m9 3.48 6 -209.486 434.6 7.22 0.011
## m11 4.06 8 -207.015 436.9 9.48 0.004
## m10 3.56 8 -209.111 441.1 13.67 0.000
## m12 4.46 11 -205.493 447.7 20.24 0.000
## Abbreviations:
## family: NB(3.322) = 'Negative Binomial(3.322)',
## NB(3.3874) = 'Negative Binomial(3.3874)',
## NB(3.4236) = 'Negative Binomial(3.4236)',
## NB(3.4258) = 'Negative Binomial(3.4258)',
## NB(3.4799) = 'Negative Binomial(3.4799)',
## NB(3.5613) = 'Negative Binomial(3.5613)',
## NB(3.8043) = 'Negative Binomial(3.8043)',
## NB(4.0552) = 'Negative Binomial(4.0552)',
## NB(4.4616) = 'Negative Binomial(4.4616)'
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 5.810 0.07016
## m3 7.091 -0.13900 0.26160 0.1758
## m2 3.820 0.35790 -0.1814
## m1 7.426 -0.16180 0.07318
## m4 3.571 0.39090 -0.1830 0.064660
## m9 5.495 0.11710 0.06617 -0.1697
## m11 4.641 0.21240 0.25510 -0.3196 0.1884
## m10 5.425 0.12660 0.16280 -0.1686 0.001496
## m12 -0.291 0.89490 0.55310 -0.3595 0.3536 0.635800
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m3
## m2
## m1
## m4
## m9 7.494e-05
## m11 8.313e-02
## m10 5.594e-03 -0.3126
## m12 1.002e-01 -0.9590
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik
## m0 2 -210.242
## m3 4 -208.185
## m2 3 -209.759
## m1 3 -209.928
## m4 4 -209.749
## m9 5 -209.504
## m11 0.1245 7 -207.315
## m10 7 -209.150
## m12 0.1373 -0.5059 10 -206.125
## AICc delta weight
## m0 424.9 0.00 0.379
## m3 426.0 1.04 0.225
## m2 426.4 1.51 0.178
## m1 426.8 1.85 0.150
## m4 429.1 4.17 0.047
## m9 431.5 6.58 0.014
## m11 433.7 8.79 0.005
## m10 437.4 12.46 0.001
## m12 443.8 18.90 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 5.8110 0.07013
## m3 7.0880 -0.13860 0.26160 0.1758
## m2 3.8200 0.35790 -0.1814
## m1 7.4260 -0.16180 0.07318
## m4 3.5710 0.39090 -0.1830 0.06464
## m9 5.4950 0.11720 0.06617 -0.1697
## m11 4.6360 0.21300 0.25510 -0.3196 0.1884
## m10 5.4240 0.12670 0.16280 -0.1686 0.00150
## m12 -0.3017 0.89640 0.55320 -0.3596 0.3537 0.63650
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m3
## m2
## m1
## m4
## m9 7.476e-05
## m11 8.322e-02
## m10 5.593e-03 -0.3127
## m12 1.002e-01 -0.9594
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 family
## m0 NB(3.322)
## m3 NB(3.8043)
## m2 NB(3.4236)
## m1 NB(3.3874)
## m4 NB(3.4258)
## m9 NB(3.4799)
## m11 0.1245 NB(4.0552)
## m10 NB(3.5613)
## m12 0.1372 -0.5063 NB(4.4616)
## init.theta df logLik AICc delta weight
## m0 3.32 3 -210.242 427.4 0.00 0.403
## m3 3.8 5 -208.042 428.6 1.18 0.224
## m2 3.42 4 -209.751 429.1 1.69 0.173
## m1 3.39 4 -209.924 429.4 2.04 0.145
## m4 3.43 5 -209.741 432.0 4.57 0.041
## m9 3.48 6 -209.486 434.6 7.22 0.011
## m11 4.06 8 -207.015 436.9 9.48 0.004
## m10 3.56 8 -209.111 441.1 13.67 0.000
## m12 4.46 11 -205.493 447.7 20.24 0.000
## Abbreviations:
## family: NB(3.322) = 'Negative Binomial(3.322)',
## NB(3.3874) = 'Negative Binomial(3.3874)',
## NB(3.4236) = 'Negative Binomial(3.4236)',
## NB(3.4258) = 'Negative Binomial(3.4258)',
## NB(3.4799) = 'Negative Binomial(3.4799)',
## NB(3.5613) = 'Negative Binomial(3.5613)',
## NB(3.8043) = 'Negative Binomial(3.8043)',
## NB(4.0552) = 'Negative Binomial(4.0552)',
## NB(4.4616) = 'Negative Binomial(4.4616)'
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 5.810 0.07016
## m3 7.091 -0.13900 0.26160 0.1758
## m2 3.820 0.35790 -0.1814
## m1 7.426 -0.16180 0.07318
## m4 3.571 0.39090 -0.1830 0.064660
## m9 5.495 0.11710 0.06617 -0.1697
## m11 4.641 0.21240 0.25510 -0.3196 0.1884
## m10 5.425 0.12660 0.16280 -0.1686 0.001496
## m12 -0.291 0.89490 0.55310 -0.3595 0.3536 0.635800
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m3
## m2
## m1
## m4
## m9 7.494e-05
## m11 8.313e-02
## m10 5.594e-03 -0.3126
## m12 1.002e-01 -0.9590
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik
## m0 2 -210.242
## m3 4 -208.185
## m2 3 -209.759
## m1 3 -209.928
## m4 4 -209.749
## m9 5 -209.504
## m11 0.1245 7 -207.315
## m10 7 -209.150
## m12 0.1373 -0.5059 10 -206.125
## AICc delta weight
## m0 424.9 0.00 0.379
## m3 426.0 1.04 0.225
## m2 426.4 1.51 0.178
## m1 426.8 1.85 0.150
## m4 429.1 4.17 0.047
## m9 431.5 6.58 0.014
## m11 433.7 8.79 0.005
## m10 437.4 12.46 0.001
## m12 443.8 18.90 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 5.8110 0.07013
## m3 7.0880 -0.13860 0.26160 0.1758
## m2 3.8200 0.35790 -0.1814
## m1 7.4260 -0.16180 0.07318
## m4 3.5710 0.39090 -0.1830 0.06464
## m9 5.4950 0.11720 0.06617 -0.1697
## m11 4.6360 0.21300 0.25510 -0.3196 0.1884
## m10 5.4240 0.12670 0.16280 -0.1686 0.00150
## m12 -0.3017 0.89640 0.55320 -0.3596 0.3537 0.63650
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m3
## m2
## m1
## m4
## m9 7.476e-05
## m11 8.322e-02
## m10 5.593e-03 -0.3127
## m12 1.002e-01 -0.9594
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 family
## m0 NB(3.322)
## m3 NB(3.8043)
## m2 NB(3.4236)
## m1 NB(3.3874)
## m4 NB(3.4258)
## m9 NB(3.4799)
## m11 0.1245 NB(4.0552)
## m10 NB(3.5613)
## m12 0.1372 -0.5063 NB(4.4616)
## init.theta df logLik AICc delta weight
## m0 3.32 3 -210.242 427.4 0.00 0.403
## m3 3.8 5 -208.042 428.6 1.18 0.224
## m2 3.42 4 -209.751 429.1 1.69 0.173
## m1 3.39 4 -209.924 429.4 2.04 0.145
## m4 3.43 5 -209.741 432.0 4.57 0.041
## m9 3.48 6 -209.486 434.6 7.22 0.011
## m11 4.06 8 -207.015 436.9 9.48 0.004
## m10 3.56 8 -209.111 441.1 13.67 0.000
## m12 4.46 11 -205.493 447.7 20.24 0.000
## Abbreviations:
## family: NB(3.322) = 'Negative Binomial(3.322)',
## NB(3.3874) = 'Negative Binomial(3.3874)',
## NB(3.4236) = 'Negative Binomial(3.4236)',
## NB(3.4258) = 'Negative Binomial(3.4258)',
## NB(3.4799) = 'Negative Binomial(3.4799)',
## NB(3.5613) = 'Negative Binomial(3.5613)',
## NB(3.8043) = 'Negative Binomial(3.8043)',
## NB(4.0552) = 'Negative Binomial(4.0552)',
## NB(4.4616) = 'Negative Binomial(4.4616)'
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 5.810 0.07016
## m3 7.091 -0.13900 0.26160 0.1758
## m2 3.820 0.35790 -0.1814
## m1 7.426 -0.16180 0.07318
## m4 3.571 0.39090 -0.1830 0.064660
## m9 5.495 0.11710 0.06617 -0.1697
## m11 4.641 0.21240 0.25510 -0.3196 0.1884
## m10 5.425 0.12660 0.16280 -0.1686 0.001496
## m12 -0.291 0.89490 0.55310 -0.3595 0.3536 0.635800
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m3
## m2
## m1
## m4
## m9 7.494e-05
## m11 8.313e-02
## m10 5.594e-03 -0.3126
## m12 1.002e-01 -0.9590
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik
## m0 2 -210.242
## m3 4 -208.185
## m2 3 -209.759
## m1 3 -209.928
## m4 4 -209.749
## m9 5 -209.504
## m11 0.1245 7 -207.315
## m10 7 -209.150
## m12 0.1373 -0.5059 10 -206.125
## AICc delta weight
## m0 424.9 0.00 0.379
## m3 426.0 1.04 0.225
## m2 426.4 1.51 0.178
## m1 426.8 1.85 0.150
## m4 429.1 4.17 0.047
## m9 431.5 6.58 0.014
## m11 433.7 8.79 0.005
## m10 437.4 12.46 0.001
## m12 443.8 18.90 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 5.8110 0.07013
## m3 7.0880 -0.13860 0.26160 0.1758
## m2 3.8200 0.35790 -0.1814
## m1 7.4260 -0.16180 0.07318
## m4 3.5710 0.39090 -0.1830 0.06464
## m9 5.4950 0.11720 0.06617 -0.1697
## m11 4.6360 0.21300 0.25510 -0.3196 0.1884
## m10 5.4240 0.12670 0.16280 -0.1686 0.00150
## m12 -0.3017 0.89640 0.55320 -0.3596 0.3537 0.63650
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m3
## m2
## m1
## m4
## m9 7.476e-05
## m11 8.322e-02
## m10 5.593e-03 -0.3127
## m12 1.002e-01 -0.9594
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 family
## m0 NB(3.322)
## m3 NB(3.8043)
## m2 NB(3.4236)
## m1 NB(3.3874)
## m4 NB(3.4258)
## m9 NB(3.4799)
## m11 0.1245 NB(4.0552)
## m10 NB(3.5613)
## m12 0.1372 -0.5063 NB(4.4616)
## init.theta df logLik AICc delta weight
## m0 3.32 3 -210.242 427.4 0.00 0.403
## m3 3.8 5 -208.042 428.6 1.18 0.224
## m2 3.42 4 -209.751 429.1 1.69 0.173
## m1 3.39 4 -209.924 429.4 2.04 0.145
## m4 3.43 5 -209.741 432.0 4.57 0.041
## m9 3.48 6 -209.486 434.6 7.22 0.011
## m11 4.06 8 -207.015 436.9 9.48 0.004
## m10 3.56 8 -209.111 441.1 13.67 0.000
## m12 4.46 11 -205.493 447.7 20.24 0.000
## Abbreviations:
## family: NB(3.322) = 'Negative Binomial(3.322)',
## NB(3.3874) = 'Negative Binomial(3.3874)',
## NB(3.4236) = 'Negative Binomial(3.4236)',
## NB(3.4258) = 'Negative Binomial(3.4258)',
## NB(3.4799) = 'Negative Binomial(3.4799)',
## NB(3.5613) = 'Negative Binomial(3.5613)',
## NB(3.8043) = 'Negative Binomial(3.8043)',
## NB(4.0552) = 'Negative Binomial(4.0552)',
## NB(4.4616) = 'Negative Binomial(4.4616)'
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 5.810 0.07016
## m3 7.091 -0.13900 0.26160 0.1758
## m2 3.820 0.35790 -0.1814
## m1 7.426 -0.16180 0.07318
## m4 3.571 0.39090 -0.1830 0.064660
## m9 5.495 0.11710 0.06617 -0.1697
## m11 4.641 0.21240 0.25510 -0.3196 0.1884
## m10 5.425 0.12660 0.16280 -0.1686 0.001496
## m12 -0.291 0.89490 0.55310 -0.3595 0.3536 0.635800
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m3
## m2
## m1
## m4
## m9 7.494e-05
## m11 8.313e-02
## m10 5.594e-03 -0.3126
## m12 1.002e-01 -0.9590
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik
## m0 2 -210.242
## m3 4 -208.185
## m2 3 -209.759
## m1 3 -209.928
## m4 4 -209.749
## m9 5 -209.504
## m11 0.1245 7 -207.315
## m10 7 -209.150
## m12 0.1373 -0.5059 10 -206.125
## AICc delta weight
## m0 424.9 0.00 0.379
## m3 426.0 1.04 0.225
## m2 426.4 1.51 0.178
## m1 426.8 1.85 0.150
## m4 429.1 4.17 0.047
## m9 431.5 6.58 0.014
## m11 433.7 8.79 0.005
## m10 437.4 12.46 0.001
## m12 443.8 18.90 0.000
## Models ranked by AICc(x)
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning in glm.nb(y ~ log(maxvol) + log(k.scalar), data = dataset):
## alternation limit reached
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning: glm.fit: algorithm did not converge
## Warning in glm.nb(y ~ log(maxvol) + log(k.scalar) + I(log(k.scalar)^2), :
## alternation limit reached
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning in glm.nb(y ~ log(maxvol) + log(mu.scalar) * log(k.scalar), data =
## dataset): alternation limit reached
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning: glm.fit: algorithm did not converge
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning: step size truncated due to divergence
## Warning in matrix(value, n, p): data length [11] is not a sub-multiple or
## multiple of the number of columns [10]
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning in glm.nb(y ~ log(maxvol) + log(k.scalar), data = dataset):
## alternation limit reached
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning: glm.fit: algorithm did not converge
## Warning in glm.nb(y ~ log(maxvol) + log(k.scalar) + I(log(k.scalar)^2), :
## alternation limit reached
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning in glm.nb(y ~ log(maxvol) + log(mu.scalar) * log(k.scalar), data =
## dataset): alternation limit reached
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning: glm.fit: algorithm did not converge
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning: step size truncated due to divergence
## Warning in matrix(value, n, p): data length [11] is not a sub-multiple or
## multiple of the number of columns [10]
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning in glm.nb(y ~ log(maxvol) + log(k.scalar), data = dataset):
## alternation limit reached
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning: glm.fit: algorithm did not converge
## Warning in glm.nb(y ~ log(maxvol) + log(k.scalar) + I(log(k.scalar)^2), :
## alternation limit reached
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning in glm.nb(y ~ log(maxvol) + log(mu.scalar) * log(k.scalar), data =
## dataset): alternation limit reached
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning: glm.fit: algorithm did not converge
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning: step size truncated due to divergence
## Warning in matrix(value, n, p): data length [11] is not a sub-multiple or
## multiple of the number of columns [10]
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m1 2.534 0.8555 -0.2358
## m0 1.576 1.0590
## m2 2.059 0.9630 0.1911
## m3 2.424 0.8893 -0.3225 -0.07340
## m4 3.039 0.8261 0.2166 -0.9522
## m9 3.043 0.7543 -0.2405 0.2074
## m10 4.709 0.4641 -0.4191 0.2368 -0.6137
## m11 1.190 1.1190 -0.2621 0.4491 -0.04310
## m12 2.543 0.8918 -0.4242 0.4763 -0.03036 -0.6533
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m1
## m0
## m2
## m3
## m4
## m9 -0.03643
## m10 -0.02346 0.6834
## m11 -0.28100
## m12 -0.31180 0.6022
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 family
## m1 NB(1.7893)
## m0 NB(1.6398)
## m2 NB(1.661)
## m3 NB(1.8089)
## m4 NB(1.7826)
## m9 NB(1.8228)
## m10 NB(2.0067)
## m11 -0.2903 NB(1.9086)
## m12 -0.2966 -0.05041 NB(2.1477)
## init.theta df logLik AICc delta weight
## m1 1.79 4 -229.449 468.6 0.00 0.368
## m0 1.64 3 -230.940 468.8 0.28 0.321
## m2 1.66 4 -230.720 471.1 2.54 0.103
## m3 1.81 5 -229.263 471.1 2.57 0.102
## m4 1.78 5 -229.513 471.6 3.07 0.079
## m9 1.82 6 -229.133 474.1 5.52 0.023
## m10 2.01 8 -227.517 478.2 9.67 0.003
## m11 1.91 8 -228.357 479.9 11.35 0.001
## m12 2.15 11 -226.387 490.3 21.74 0.000
## Abbreviations:
## family: NB(1.6398) = 'Negative Binomial(1.6398)',
## NB(1.661) = 'Negative Binomial(1.661)',
## NB(1.7826) = 'Negative Binomial(1.7826)',
## NB(1.7893) = 'Negative Binomial(1.7893)',
## NB(1.8089) = 'Negative Binomial(1.8089)',
## NB(1.8228) = 'Negative Binomial(1.8228)',
## NB(1.9086) = 'Negative Binomial(1.9086)',
## NB(2.0067) = 'Negative Binomial(2.0067)',
## NB(2.1477) = 'Negative Binomial(2.1477)'
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m1 2.534 0.8555 -0.2358
## m0 1.576 1.0590
## m3 2.424 0.8892 -0.3225 -0.07341
## m2 2.059 0.9630 0.1911
## m4 3.039 0.8262 0.2166 -0.9522
## m9 3.043 0.7543 -0.2405 0.2074
## m10 4.709 0.4642 -0.4191 0.2368 -0.6137
## m11 1.189 1.1190 -0.2620 0.4491 -0.04308
## m12 2.543 0.8918 -0.4242 0.4763 -0.03037 -0.6533
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m1
## m0
## m3
## m2
## m4
## m9 -0.03645
## m10 -0.02348 0.6834
## m11 -0.28100
## m12 -0.31190 0.6022
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik
## m1 3 -229.449
## m0 2 -231.008
## m3 4 -229.264
## m2 3 -230.769
## m4 4 -229.513
## m9 5 -229.136
## m10 7 -227.623
## m11 -0.2903 7 -228.391
## m12 -0.2966 -0.05037 10 -226.648
## AICc delta weight
## m1 465.9 0.00 0.377
## m0 466.5 0.62 0.277
## m3 468.2 2.34 0.117
## m2 468.5 2.64 0.101
## m4 468.7 2.84 0.091
## m9 470.9 5.02 0.031
## m10 474.6 8.72 0.005
## m11 476.1 10.26 0.002
## m12 485.5 19.66 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m1 2.534 0.8555 -0.2358
## m0 1.576 1.0590
## m2 2.059 0.9630 0.1911
## m3 2.424 0.8893 -0.3225 -0.07340
## m4 3.039 0.8261 0.2166 -0.9522
## m9 3.043 0.7543 -0.2405 0.2074
## m10 4.709 0.4641 -0.4191 0.2368 -0.6137
## m11 1.190 1.1190 -0.2621 0.4491 -0.04310
## m12 2.543 0.8918 -0.4242 0.4763 -0.03036 -0.6533
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m1
## m0
## m2
## m3
## m4
## m9 -0.03643
## m10 -0.02346 0.6834
## m11 -0.28100
## m12 -0.31180 0.6022
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 family
## m1 NB(1.7893)
## m0 NB(1.6398)
## m2 NB(1.661)
## m3 NB(1.8089)
## m4 NB(1.7826)
## m9 NB(1.8228)
## m10 NB(2.0067)
## m11 -0.2903 NB(1.9086)
## m12 -0.2966 -0.05041 NB(2.1477)
## init.theta df logLik AICc delta weight
## m1 1.79 4 -229.449 468.6 0.00 0.368
## m0 1.64 3 -230.940 468.8 0.28 0.321
## m2 1.66 4 -230.720 471.1 2.54 0.103
## m3 1.81 5 -229.263 471.1 2.57 0.102
## m4 1.78 5 -229.513 471.6 3.07 0.079
## m9 1.82 6 -229.133 474.1 5.52 0.023
## m10 2.01 8 -227.517 478.2 9.67 0.003
## m11 1.91 8 -228.357 479.9 11.35 0.001
## m12 2.15 11 -226.387 490.3 21.74 0.000
## Abbreviations:
## family: NB(1.6398) = 'Negative Binomial(1.6398)',
## NB(1.661) = 'Negative Binomial(1.661)',
## NB(1.7826) = 'Negative Binomial(1.7826)',
## NB(1.7893) = 'Negative Binomial(1.7893)',
## NB(1.8089) = 'Negative Binomial(1.8089)',
## NB(1.8228) = 'Negative Binomial(1.8228)',
## NB(1.9086) = 'Negative Binomial(1.9086)',
## NB(2.0067) = 'Negative Binomial(2.0067)',
## NB(2.1477) = 'Negative Binomial(2.1477)'
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m1 2.534 0.8555 -0.2358
## m0 1.576 1.0590
## m3 2.424 0.8892 -0.3225 -0.07341
## m2 2.059 0.9630 0.1911
## m4 3.039 0.8262 0.2166 -0.9522
## m9 3.043 0.7543 -0.2405 0.2074
## m10 4.709 0.4642 -0.4191 0.2368 -0.6137
## m11 1.189 1.1190 -0.2620 0.4491 -0.04308
## m12 2.543 0.8918 -0.4242 0.4763 -0.03037 -0.6533
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m1
## m0
## m3
## m2
## m4
## m9 -0.03645
## m10 -0.02348 0.6834
## m11 -0.28100
## m12 -0.31190 0.6022
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik
## m1 3 -229.449
## m0 2 -231.008
## m3 4 -229.264
## m2 3 -230.769
## m4 4 -229.513
## m9 5 -229.136
## m10 7 -227.623
## m11 -0.2903 7 -228.391
## m12 -0.2966 -0.05037 10 -226.648
## AICc delta weight
## m1 465.9 0.00 0.377
## m0 466.5 0.62 0.277
## m3 468.2 2.34 0.117
## m2 468.5 2.64 0.101
## m4 468.7 2.84 0.091
## m9 470.9 5.02 0.031
## m10 474.6 8.72 0.005
## m11 476.1 10.26 0.002
## m12 485.5 19.66 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m2 -1.0200 1.230 0.7159
## m4 -2.2240 1.494 0.8573 -1.1280
## m0 0.3052 1.016
## m1 0.2006 1.031 -0.05653
## m9 -1.0990 1.234 -0.14530 0.7740
## m3 -0.2368 1.137 -0.35500 -0.2368
## m10 -2.6840 1.541 -0.47370 0.9043 -0.7301
## m11 -1.3530 1.312 -0.46350 0.8233 -0.2658
## m12 -3.0830 1.653 -0.77970 1.0180 -0.2915 -0.8096
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m2
## m4
## m0
## m1
## m9 0.20610
## m3
## m10 0.13160 1.152
## m11 0.17760
## m12 0.03313 1.055
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 family
## m2 NB(1.3886)
## m4 NB(1.4707)
## m0 NB(1.2208)
## m1 NB(1.2242)
## m9 NB(1.4313)
## m3 NB(1.2945)
## m10 NB(1.6079)
## m11 -0.01002 NB(1.579)
## m12 -0.07736 -0.03503 NB(1.857)
## init.theta df logLik AICc delta weight
## m2 1.39 4 -206.262 422.2 0.00 0.434
## m4 1.47 5 -205.257 423.1 0.93 0.272
## m0 1.22 3 -208.540 424.0 1.85 0.172
## m1 1.22 4 -208.489 426.6 4.45 0.047
## m9 1.43 6 -205.729 427.3 5.09 0.034
## m3 1.29 5 -207.487 427.6 5.39 0.029
## m10 1.61 8 -203.709 430.6 8.43 0.006
## m11 1.58 8 -204.022 431.2 9.05 0.005
## m12 1.86 11 -201.275 440.1 17.89 0.000
## Abbreviations:
## family: NB(1.2208) = 'Negative Binomial(1.2208)',
## NB(1.2242) = 'Negative Binomial(1.2242)',
## NB(1.2945) = 'Negative Binomial(1.2945)',
## NB(1.3886) = 'Negative Binomial(1.3886)',
## NB(1.4313) = 'Negative Binomial(1.4313)',
## NB(1.4707) = 'Negative Binomial(1.4707)',
## NB(1.579) = 'Negative Binomial(1.579)',
## NB(1.6079) = 'Negative Binomial(1.6079)',
## NB(1.857) = 'Negative Binomial(1.857)'
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m2 -1.0200 1.230 0.7159
## m4 -2.2250 1.494 0.8574 -1.1290
## m0 0.3062 1.015
## m9 -1.1000 1.235 -0.14540 0.7740
## m1 0.2015 1.031 -0.05644
## m3 -0.2354 1.137 -0.35470 -0.2367
## m10 -2.6870 1.541 -0.47410 0.9046 -0.7301
## m11 -1.3550 1.312 -0.46380 0.8236 -0.2659
## m12 -3.0790 1.652 -0.77800 1.0180 -0.2908 -0.8094
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m2
## m4
## m0
## m9 0.20600
## m1
## m3
## m10 0.13140 1.153
## m11 0.17730
## m12 0.03283 1.051
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik
## m2 3 -206.262
## m4 4 -205.284
## m0 2 -208.696
## m9 5 -205.737
## m1 3 -208.638
## m3 4 -207.532
## m10 7 -203.882
## m11 -0.01016 7 -204.156
## m12 -0.07746 -0.03664 10 -201.920
## AICc delta weight
## m2 419.5 0.00 0.435
## m4 420.2 0.75 0.299
## m0 421.9 2.37 0.133
## m9 424.1 4.60 0.044
## m1 424.2 4.75 0.040
## m3 424.7 5.25 0.032
## m10 427.1 7.61 0.010
## m11 427.6 8.16 0.007
## m12 436.1 16.58 0.000
## Models ranked by AICc(x)
## Warning: glm.fit: algorithm did not converge
## Model selection table
## (Int) log(mxv) cv.dpt prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m0 -0.6308 0.8430
## m5 2.5140 0.3917 -0.0904
## m6 0.9524 0.6208
## m2 0.2275 0.6700 0.8191
## m4 -0.9945 0.9216 -0.002827
## m3 -0.5149 0.8258 -0.0902
## m1 -0.5134 0.8272 -0.0004433
## lst_wet family init.theta df logLik AICc delta weight
## m0 NB(0.4279) 0.428 3 -152.337 311.6 0.00 0.331
## m5 NB(0.4475) 0.447 4 -151.555 312.7 1.11 0.190
## m6 -0.02223 NB(0.4388) 0.439 4 -151.946 313.5 1.90 0.128
## m2 NB(0.429) 0.429 4 -152.298 314.2 2.60 0.090
## m4 NB(0.4281) 0.428 4 -152.329 314.3 2.66 0.087
## m3 NB(0.4279) 0.428 4 -152.336 314.3 2.67 0.087
## m1 NB(0.4279) 0.428 4 -152.336 314.3 2.68 0.087
## Abbreviations:
## family: NB(0.4279) = 'Negative Binomial(0.4279)',
## NB(0.4281) = 'Negative Binomial(0.4281)',
## NB(0.429) = 'Negative Binomial(0.429)',
## NB(0.4388) = 'Negative Binomial(0.4388)',
## NB(0.4475) = 'Negative Binomial(0.4475)'
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m5 2.5280 0.3899 -0.09091
## m6 0.9524 0.6208
## m2 0.2270 0.6701 0.8188
## m4 -0.9952 0.9218 -0.002831
## m3 -0.5155 0.8259 -0.08982
## m1 -0.5122 -0.0004473 0.8271
## lst_wet df logLik AICc delta weight
## m5 3 -151.570 310.1 0.00 0.280
## m6 -0.02223 3 -151.951 310.8 0.76 0.192
## m2 3 -152.298 311.5 1.45 0.136
## m4 3 -152.329 311.6 1.52 0.131
## m3 3 -152.336 311.6 1.53 0.130
## m1 3 -152.336 311.6 1.53 0.130
## Models ranked by AICc(x)
## Warning: glm.fit: algorithm did not converge
## Model selection table
## (Int) log(mxv) cv.dpt prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m0 -0.6308 0.8430
## m5 2.5140 0.3917 -0.0904
## m6 0.9524 0.6208
## m2 0.2275 0.6700 0.8191
## m4 -0.9945 0.9216 -0.002827
## m3 -0.5149 0.8258 -0.0902
## m1 -0.5134 0.8272 -0.0004433
## lst_wet family init.theta df logLik AICc delta weight
## m0 NB(0.4279) 0.428 3 -152.337 311.6 0.00 0.331
## m5 NB(0.4475) 0.447 4 -151.555 312.7 1.11 0.190
## m6 -0.02223 NB(0.4388) 0.439 4 -151.946 313.5 1.90 0.128
## m2 NB(0.429) 0.429 4 -152.298 314.2 2.60 0.090
## m4 NB(0.4281) 0.428 4 -152.329 314.3 2.66 0.087
## m3 NB(0.4279) 0.428 4 -152.336 314.3 2.67 0.087
## m1 NB(0.4279) 0.428 4 -152.336 314.3 2.68 0.087
## Abbreviations:
## family: NB(0.4279) = 'Negative Binomial(0.4279)',
## NB(0.4281) = 'Negative Binomial(0.4281)',
## NB(0.429) = 'Negative Binomial(0.429)',
## NB(0.4388) = 'Negative Binomial(0.4388)',
## NB(0.4475) = 'Negative Binomial(0.4475)'
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m5 2.5280 0.3899 -0.09091
## m6 0.9524 0.6208
## m2 0.2270 0.6701 0.8188
## m4 -0.9952 0.9218 -0.002831
## m3 -0.5155 0.8259 -0.08982
## m1 -0.5122 -0.0004473 0.8271
## lst_wet df logLik AICc delta weight
## m5 3 -151.570 310.1 0.00 0.280
## m6 -0.02223 3 -151.951 310.8 0.76 0.192
## m2 3 -152.298 311.5 1.45 0.136
## m4 3 -152.329 311.6 1.52 0.131
## m3 3 -152.336 311.6 1.53 0.130
## m1 3 -152.336 311.6 1.53 0.130
## Models ranked by AICc(x)
## Warning: glm.fit: algorithm did not converge
## Model selection table
## (Int) log(mxv) cv.dpt prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m0 -0.6308 0.8430
## m5 2.5140 0.3917 -0.0904
## m6 0.9524 0.6208
## m2 0.2275 0.6700 0.8191
## m4 -0.9945 0.9216 -0.002827
## m3 -0.5149 0.8258 -0.0902
## m1 -0.5134 0.8272 -0.0004433
## lst_wet family init.theta df logLik AICc delta weight
## m0 NB(0.4279) 0.428 3 -152.337 311.6 0.00 0.331
## m5 NB(0.4475) 0.447 4 -151.555 312.7 1.11 0.190
## m6 -0.02223 NB(0.4388) 0.439 4 -151.946 313.5 1.90 0.128
## m2 NB(0.429) 0.429 4 -152.298 314.2 2.60 0.090
## m4 NB(0.4281) 0.428 4 -152.329 314.3 2.66 0.087
## m3 NB(0.4279) 0.428 4 -152.336 314.3 2.67 0.087
## m1 NB(0.4279) 0.428 4 -152.336 314.3 2.68 0.087
## Abbreviations:
## family: NB(0.4279) = 'Negative Binomial(0.4279)',
## NB(0.4281) = 'Negative Binomial(0.4281)',
## NB(0.429) = 'Negative Binomial(0.429)',
## NB(0.4388) = 'Negative Binomial(0.4388)',
## NB(0.4475) = 'Negative Binomial(0.4475)'
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m5 2.5280 0.3899 -0.09091
## m6 0.9524 0.6208
## m2 0.2270 0.6701 0.8188
## m4 -0.9952 0.9218 -0.002831
## m3 -0.5155 0.8259 -0.08982
## m1 -0.5122 -0.0004473 0.8271
## lst_wet df logLik AICc delta weight
## m5 3 -151.570 310.1 0.00 0.280
## m6 -0.02223 3 -151.951 310.8 0.76 0.192
## m2 3 -152.298 311.5 1.45 0.136
## m4 3 -152.329 311.6 1.52 0.131
## m3 3 -152.336 311.6 1.53 0.130
## m1 3 -152.336 311.6 1.53 0.130
## Models ranked by AICc(x)
## Warning: glm.fit: algorithm did not converge
## Model selection table
## (Int) log(mxv) cv.dpt prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m0 -0.6308 0.8430
## m5 2.5140 0.3917 -0.0904
## m6 0.9524 0.6208
## m2 0.2275 0.6700 0.8191
## m4 -0.9945 0.9216 -0.002827
## m3 -0.5149 0.8258 -0.0902
## m1 -0.5134 0.8272 -0.0004433
## lst_wet family init.theta df logLik AICc delta weight
## m0 NB(0.4279) 0.428 3 -152.337 311.6 0.00 0.331
## m5 NB(0.4475) 0.447 4 -151.555 312.7 1.11 0.190
## m6 -0.02223 NB(0.4388) 0.439 4 -151.946 313.5 1.90 0.128
## m2 NB(0.429) 0.429 4 -152.298 314.2 2.60 0.090
## m4 NB(0.4281) 0.428 4 -152.329 314.3 2.66 0.087
## m3 NB(0.4279) 0.428 4 -152.336 314.3 2.67 0.087
## m1 NB(0.4279) 0.428 4 -152.336 314.3 2.68 0.087
## Abbreviations:
## family: NB(0.4279) = 'Negative Binomial(0.4279)',
## NB(0.4281) = 'Negative Binomial(0.4281)',
## NB(0.429) = 'Negative Binomial(0.429)',
## NB(0.4388) = 'Negative Binomial(0.4388)',
## NB(0.4475) = 'Negative Binomial(0.4475)'
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m5 2.5280 0.3899 -0.09091
## m6 0.9524 0.6208
## m2 0.2270 0.6701 0.8188
## m4 -0.9952 0.9218 -0.002831
## m3 -0.5155 0.8259 -0.08982
## m1 -0.5122 -0.0004473 0.8271
## lst_wet df logLik AICc delta weight
## m5 3 -151.570 310.1 0.00 0.280
## m6 -0.02223 3 -151.951 310.8 0.76 0.192
## m2 3 -152.298 311.5 1.45 0.136
## m4 3 -152.329 311.6 1.52 0.131
## m3 3 -152.336 311.6 1.53 0.130
## m1 3 -152.336 311.6 1.53 0.130
## Models ranked by AICc(x)
## Warning: glm.fit: algorithm did not converge
## Model selection table
## (Int) log(mxv) cv.dpt prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m0 -0.6308 0.8430
## m5 2.5140 0.3917 -0.0904
## m6 0.9524 0.6208
## m2 0.2275 0.6700 0.8191
## m4 -0.9945 0.9216 -0.002827
## m3 -0.5149 0.8258 -0.0902
## m1 -0.5134 0.8272 -0.0004433
## lst_wet family init.theta df logLik AICc delta weight
## m0 NB(0.4279) 0.428 3 -152.337 311.6 0.00 0.331
## m5 NB(0.4475) 0.447 4 -151.555 312.7 1.11 0.190
## m6 -0.02223 NB(0.4388) 0.439 4 -151.946 313.5 1.90 0.128
## m2 NB(0.429) 0.429 4 -152.298 314.2 2.60 0.090
## m4 NB(0.4281) 0.428 4 -152.329 314.3 2.66 0.087
## m3 NB(0.4279) 0.428 4 -152.336 314.3 2.67 0.087
## m1 NB(0.4279) 0.428 4 -152.336 314.3 2.68 0.087
## Abbreviations:
## family: NB(0.4279) = 'Negative Binomial(0.4279)',
## NB(0.4281) = 'Negative Binomial(0.4281)',
## NB(0.429) = 'Negative Binomial(0.429)',
## NB(0.4388) = 'Negative Binomial(0.4388)',
## NB(0.4475) = 'Negative Binomial(0.4475)'
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m5 2.5280 0.3899 -0.09091
## m6 0.9524 0.6208
## m2 0.2270 0.6701 0.8188
## m4 -0.9952 0.9218 -0.002831
## m3 -0.5155 0.8259 -0.08982
## m1 -0.5122 -0.0004473 0.8271
## lst_wet df logLik AICc delta weight
## m5 3 -151.570 310.1 0.00 0.280
## m6 -0.02223 3 -151.951 310.8 0.76 0.192
## m2 3 -152.298 311.5 1.45 0.136
## m4 3 -152.329 311.6 1.52 0.131
## m3 3 -152.336 311.6 1.53 0.130
## m1 3 -152.336 311.6 1.53 0.130
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) cv.dpt prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m0 5.811 0.070130
## m2 6.921 -0.119000 2.973
## m6 6.710 -0.040110
## m3 6.176 0.013530 0.479
## m5 6.258 0.001927 0.01151
## m4 5.760 0.051540 0.002762
## m1 5.838 0.065120 0.000182
## lst_wet family init.theta df logLik AICc delta weight
## m0 NB(3.322) 3.32 3 -210.242 427.4 0.00 0.314
## m2 NB(3.4713) 3.47 4 -209.526 428.7 1.24 0.169
## m6 -0.004714 NB(3.4113) 3.41 4 -209.809 429.2 1.81 0.127
## m3 NB(3.3743) 3.37 4 -209.988 429.6 2.17 0.106
## m5 NB(3.3659) 3.37 4 -210.028 429.7 2.25 0.102
## m4 NB(3.3598) 3.36 4 -210.058 429.7 2.31 0.099
## m1 NB(3.3225) 3.32 4 -210.240 430.1 2.67 0.083
## Abbreviations:
## family: NB(3.322) = 'Negative Binomial(3.322)',
## NB(3.3225) = 'Negative Binomial(3.3225)',
## NB(3.3598) = 'Negative Binomial(3.3598)',
## NB(3.3659) = 'Negative Binomial(3.3659)',
## NB(3.3743) = 'Negative Binomial(3.3743)',
## NB(3.4113) = 'Negative Binomial(3.4113)',
## NB(3.4713) = 'Negative Binomial(3.4713)'
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m2 6.922 -0.119000 2.973
## m6 6.710 -0.040120
## m3 6.176 0.013580 0.479
## m5 6.258 0.001926 0.01151
## m4 5.760 0.051550 0.002762
## m1 5.838 0.0001819 0.065130
## lst_wet df logLik AICc delta weight
## m2 3 -209.542 426.0 0.00 0.243
## m6 -0.004714 3 -209.815 426.6 0.55 0.185
## m3 3 -209.990 426.9 0.90 0.156
## m5 3 -210.030 427.0 0.98 0.149
## m4 3 -210.059 427.0 1.03 0.145
## m1 3 -210.240 427.4 1.40 0.121
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) cv.dpt prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m0 5.811 0.070130
## m2 6.921 -0.119000 2.973
## m6 6.710 -0.040110
## m3 6.176 0.013530 0.479
## m5 6.258 0.001927 0.01151
## m4 5.760 0.051540 0.002762
## m1 5.838 0.065120 0.000182
## lst_wet family init.theta df logLik AICc delta weight
## m0 NB(3.322) 3.32 3 -210.242 427.4 0.00 0.314
## m2 NB(3.4713) 3.47 4 -209.526 428.7 1.24 0.169
## m6 -0.004714 NB(3.4113) 3.41 4 -209.809 429.2 1.81 0.127
## m3 NB(3.3743) 3.37 4 -209.988 429.6 2.17 0.106
## m5 NB(3.3659) 3.37 4 -210.028 429.7 2.25 0.102
## m4 NB(3.3598) 3.36 4 -210.058 429.7 2.31 0.099
## m1 NB(3.3225) 3.32 4 -210.240 430.1 2.67 0.083
## Abbreviations:
## family: NB(3.322) = 'Negative Binomial(3.322)',
## NB(3.3225) = 'Negative Binomial(3.3225)',
## NB(3.3598) = 'Negative Binomial(3.3598)',
## NB(3.3659) = 'Negative Binomial(3.3659)',
## NB(3.3743) = 'Negative Binomial(3.3743)',
## NB(3.4113) = 'Negative Binomial(3.4113)',
## NB(3.4713) = 'Negative Binomial(3.4713)'
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m2 6.922 -0.119000 2.973
## m6 6.710 -0.040120
## m3 6.176 0.013580 0.479
## m5 6.258 0.001926 0.01151
## m4 5.760 0.051550 0.002762
## m1 5.838 0.0001819 0.065130
## lst_wet df logLik AICc delta weight
## m2 3 -209.542 426.0 0.00 0.243
## m6 -0.004714 3 -209.815 426.6 0.55 0.185
## m3 3 -209.990 426.9 0.90 0.156
## m5 3 -210.030 427.0 0.98 0.149
## m4 3 -210.059 427.0 1.03 0.145
## m1 3 -210.240 427.4 1.40 0.121
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) cv.dpt prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m0 5.811 0.070130
## m2 6.921 -0.119000 2.973
## m6 6.710 -0.040110
## m3 6.176 0.013530 0.479
## m5 6.258 0.001927 0.01151
## m4 5.760 0.051540 0.002762
## m1 5.838 0.065120 0.000182
## lst_wet family init.theta df logLik AICc delta weight
## m0 NB(3.322) 3.32 3 -210.242 427.4 0.00 0.314
## m2 NB(3.4713) 3.47 4 -209.526 428.7 1.24 0.169
## m6 -0.004714 NB(3.4113) 3.41 4 -209.809 429.2 1.81 0.127
## m3 NB(3.3743) 3.37 4 -209.988 429.6 2.17 0.106
## m5 NB(3.3659) 3.37 4 -210.028 429.7 2.25 0.102
## m4 NB(3.3598) 3.36 4 -210.058 429.7 2.31 0.099
## m1 NB(3.3225) 3.32 4 -210.240 430.1 2.67 0.083
## Abbreviations:
## family: NB(3.322) = 'Negative Binomial(3.322)',
## NB(3.3225) = 'Negative Binomial(3.3225)',
## NB(3.3598) = 'Negative Binomial(3.3598)',
## NB(3.3659) = 'Negative Binomial(3.3659)',
## NB(3.3743) = 'Negative Binomial(3.3743)',
## NB(3.4113) = 'Negative Binomial(3.4113)',
## NB(3.4713) = 'Negative Binomial(3.4713)'
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m2 6.922 -0.119000 2.973
## m6 6.710 -0.040120
## m3 6.176 0.013580 0.479
## m5 6.258 0.001926 0.01151
## m4 5.760 0.051550 0.002762
## m1 5.838 0.0001819 0.065130
## lst_wet df logLik AICc delta weight
## m2 3 -209.542 426.0 0.00 0.243
## m6 -0.004714 3 -209.815 426.6 0.55 0.185
## m3 3 -209.990 426.9 0.90 0.156
## m5 3 -210.030 427.0 0.98 0.149
## m4 3 -210.059 427.0 1.03 0.145
## m1 3 -210.240 427.4 1.40 0.121
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) cv.dpt prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m0 5.811 0.070130
## m2 6.921 -0.119000 2.973
## m6 6.710 -0.040110
## m3 6.176 0.013530 0.479
## m5 6.258 0.001927 0.01151
## m4 5.760 0.051540 0.002762
## m1 5.838 0.065120 0.000182
## lst_wet family init.theta df logLik AICc delta weight
## m0 NB(3.322) 3.32 3 -210.242 427.4 0.00 0.314
## m2 NB(3.4713) 3.47 4 -209.526 428.7 1.24 0.169
## m6 -0.004714 NB(3.4113) 3.41 4 -209.809 429.2 1.81 0.127
## m3 NB(3.3743) 3.37 4 -209.988 429.6 2.17 0.106
## m5 NB(3.3659) 3.37 4 -210.028 429.7 2.25 0.102
## m4 NB(3.3598) 3.36 4 -210.058 429.7 2.31 0.099
## m1 NB(3.3225) 3.32 4 -210.240 430.1 2.67 0.083
## Abbreviations:
## family: NB(3.322) = 'Negative Binomial(3.322)',
## NB(3.3225) = 'Negative Binomial(3.3225)',
## NB(3.3598) = 'Negative Binomial(3.3598)',
## NB(3.3659) = 'Negative Binomial(3.3659)',
## NB(3.3743) = 'Negative Binomial(3.3743)',
## NB(3.4113) = 'Negative Binomial(3.4113)',
## NB(3.4713) = 'Negative Binomial(3.4713)'
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m2 6.922 -0.119000 2.973
## m6 6.710 -0.040120
## m3 6.176 0.013580 0.479
## m5 6.258 0.001926 0.01151
## m4 5.760 0.051550 0.002762
## m1 5.838 0.0001819 0.065130
## lst_wet df logLik AICc delta weight
## m2 3 -209.542 426.0 0.00 0.243
## m6 -0.004714 3 -209.815 426.6 0.55 0.185
## m3 3 -209.990 426.9 0.90 0.156
## m5 3 -210.030 427.0 0.98 0.149
## m4 3 -210.059 427.0 1.03 0.145
## m1 3 -210.240 427.4 1.40 0.121
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) cv.dpt prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m6 7.336 -0.8427
## m5 8.141 -1.0030 -0.282
## m0 4.761 -0.4174
## m1 11.290 -1.4810 -0.03233
## m4 8.319 -1.3480 0.02943
## m3 6.652 -0.7463 -6.709
## m2 4.945 -0.4703 0.4098
## lst_wet family init.theta df logLik AICc delta weight
## m6 -0.07483 NB(0.6663) 0.666 4 -103.125 215.8 0.00 0.225
## m5 NB(0.6617) 0.662 4 -103.142 215.9 0.03 0.221
## m0 NB(0.5996) 0.6 3 -104.587 216.1 0.25 0.199
## m1 NB(0.6415) 0.642 4 -103.699 217.0 1.15 0.127
## m4 NB(0.6237) 0.624 4 -104.072 217.7 1.90 0.087
## m3 NB(0.6235) 0.623 4 -104.078 217.8 1.91 0.087
## m2 NB(0.6008) 0.601 4 -104.561 218.7 2.87 0.054
## Abbreviations:
## family: NB(0.5996) = 'Negative Binomial(0.5996)',
## NB(0.6008) = 'Negative Binomial(0.6008)',
## NB(0.6235) = 'Negative Binomial(0.6235)',
## NB(0.6237) = 'Negative Binomial(0.6237)',
## NB(0.6415) = 'Negative Binomial(0.6415)',
## NB(0.6617) = 'Negative Binomial(0.6617)',
## NB(0.6663) = 'Negative Binomial(0.6663)'
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m6 7.336 -0.8425
## m5 8.137 -1.0020 -0.2818
## m1 11.280 -0.03227 -1.4790
## m4 8.311 -1.3450 0.02935
## m3 6.651 -0.7461 -6.696
## m2 4.950 -0.4712 0.4089
## lst_wet df logLik AICc delta weight
## m6 -0.07483 3 -103.125 213.2 0.00 0.284
## m5 3 -103.142 213.2 0.03 0.279
## m1 3 -103.708 214.3 1.17 0.159
## m4 3 -104.098 215.1 1.95 0.107
## m3 3 -104.104 215.1 1.96 0.107
## m2 3 -104.626 216.2 3.00 0.063
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) cv.dpt prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m6 7.336 -0.8427
## m5 8.141 -1.0030 -0.282
## m0 4.761 -0.4174
## m1 11.290 -1.4810 -0.03233
## m4 8.319 -1.3480 0.02943
## m3 6.652 -0.7463 -6.709
## m2 4.945 -0.4703 0.4098
## lst_wet family init.theta df logLik AICc delta weight
## m6 -0.07483 NB(0.6663) 0.666 4 -103.125 215.8 0.00 0.225
## m5 NB(0.6617) 0.662 4 -103.142 215.9 0.03 0.221
## m0 NB(0.5996) 0.6 3 -104.587 216.1 0.25 0.199
## m1 NB(0.6415) 0.642 4 -103.699 217.0 1.15 0.127
## m4 NB(0.6237) 0.624 4 -104.072 217.7 1.90 0.087
## m3 NB(0.6235) 0.623 4 -104.078 217.8 1.91 0.087
## m2 NB(0.6008) 0.601 4 -104.561 218.7 2.87 0.054
## Abbreviations:
## family: NB(0.5996) = 'Negative Binomial(0.5996)',
## NB(0.6008) = 'Negative Binomial(0.6008)',
## NB(0.6235) = 'Negative Binomial(0.6235)',
## NB(0.6237) = 'Negative Binomial(0.6237)',
## NB(0.6415) = 'Negative Binomial(0.6415)',
## NB(0.6617) = 'Negative Binomial(0.6617)',
## NB(0.6663) = 'Negative Binomial(0.6663)'
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m6 7.336 -0.8425
## m5 8.137 -1.0020 -0.2818
## m1 11.280 -0.03227 -1.4790
## m4 8.311 -1.3450 0.02935
## m3 6.651 -0.7461 -6.696
## m2 4.950 -0.4712 0.4089
## lst_wet df logLik AICc delta weight
## m6 -0.07483 3 -103.125 213.2 0.00 0.284
## m5 3 -103.142 213.2 0.03 0.279
## m1 3 -103.708 214.3 1.17 0.159
## m4 3 -104.098 215.1 1.95 0.107
## m3 3 -104.104 215.1 1.96 0.107
## m2 3 -104.626 216.2 3.00 0.063
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) cv.dpt prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m6 7.336 -0.8427
## m5 8.141 -1.0030 -0.282
## m0 4.761 -0.4174
## m1 11.290 -1.4810 -0.03233
## m4 8.319 -1.3480 0.02943
## m3 6.652 -0.7463 -6.709
## m2 4.945 -0.4703 0.4098
## lst_wet family init.theta df logLik AICc delta weight
## m6 -0.07483 NB(0.6663) 0.666 4 -103.125 215.8 0.00 0.225
## m5 NB(0.6617) 0.662 4 -103.142 215.9 0.03 0.221
## m0 NB(0.5996) 0.6 3 -104.587 216.1 0.25 0.199
## m1 NB(0.6415) 0.642 4 -103.699 217.0 1.15 0.127
## m4 NB(0.6237) 0.624 4 -104.072 217.7 1.90 0.087
## m3 NB(0.6235) 0.623 4 -104.078 217.8 1.91 0.087
## m2 NB(0.6008) 0.601 4 -104.561 218.7 2.87 0.054
## Abbreviations:
## family: NB(0.5996) = 'Negative Binomial(0.5996)',
## NB(0.6008) = 'Negative Binomial(0.6008)',
## NB(0.6235) = 'Negative Binomial(0.6235)',
## NB(0.6237) = 'Negative Binomial(0.6237)',
## NB(0.6415) = 'Negative Binomial(0.6415)',
## NB(0.6617) = 'Negative Binomial(0.6617)',
## NB(0.6663) = 'Negative Binomial(0.6663)'
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m6 7.336 -0.8425
## m5 8.137 -1.0020 -0.2818
## m1 11.280 -0.03227 -1.4790
## m4 8.311 -1.3450 0.02935
## m3 6.651 -0.7461 -6.696
## m2 4.950 -0.4712 0.4089
## lst_wet df logLik AICc delta weight
## m6 -0.07483 3 -103.125 213.2 0.00 0.284
## m5 3 -103.142 213.2 0.03 0.279
## m1 3 -103.708 214.3 1.17 0.159
## m4 3 -104.098 215.1 1.95 0.107
## m3 3 -104.104 215.1 1.96 0.107
## m2 3 -104.626 216.2 3.00 0.063
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) cv.dpt prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m2 1.3510 0.9933 2.585
## m0 1.5760 1.0590
## m6 1.0560 1.1950
## m1 3.4220 0.7659 -0.005775
## m5 2.3600 0.9246 -0.06198
## m3 2.5880 0.8774 -0.3827
## m4 0.9765 1.2000 -0.006728
## lst_wet family init.theta df logLik AICc delta weight
## m2 NB(1.916) 1.92 4 -228.292 466.3 0.00 0.550
## m0 NB(1.6398) 1.64 3 -230.940 468.8 2.59 0.151
## m6 -0.01259 NB(1.7382) 1.74 4 -229.942 469.5 3.30 0.106
## m1 NB(1.6841) 1.68 4 -230.482 470.6 4.38 0.062
## m5 NB(1.6552) 1.66 4 -230.780 471.2 4.98 0.046
## m3 NB(1.6542) 1.65 4 -230.790 471.2 5.00 0.045
## m4 NB(1.6451) 1.65 4 -230.885 471.4 5.19 0.041
## Abbreviations:
## family: NB(1.6398) = 'Negative Binomial(1.6398)',
## NB(1.6451) = 'Negative Binomial(1.6451)',
## NB(1.6542) = 'Negative Binomial(1.6542)',
## NB(1.6552) = 'Negative Binomial(1.6552)',
## NB(1.6841) = 'Negative Binomial(1.6841)',
## NB(1.7382) = 'Negative Binomial(1.7382)',
## NB(1.916) = 'Negative Binomial(1.916)'
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m2 1.3510 0.9933 2.585
## m6 1.0560 1.1950
## m1 3.4220 -0.005775 0.7659
## m5 2.3600 0.9246 -0.06198
## m3 2.5880 0.8774 -0.3827
## m4 0.9766 1.2000 -0.006725
## lst_wet df logLik AICc delta weight
## m2 3 -228.292 463.5 0.00 0.681
## m6 -0.01259 3 -230.028 467.0 3.47 0.120
## m1 3 -230.635 468.2 4.69 0.065
## m5 3 -230.977 468.9 5.37 0.046
## m3 3 -230.988 468.9 5.39 0.046
## m4 3 -231.100 469.2 5.62 0.041
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) cv.dpt prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m2 1.3510 0.9933 2.585
## m0 1.5760 1.0590
## m6 1.0560 1.1950
## m1 3.4220 0.7659 -0.005775
## m5 2.3600 0.9246 -0.06198
## m3 2.5880 0.8774 -0.3827
## m4 0.9765 1.2000 -0.006728
## lst_wet family init.theta df logLik AICc delta weight
## m2 NB(1.916) 1.92 4 -228.292 466.3 0.00 0.550
## m0 NB(1.6398) 1.64 3 -230.940 468.8 2.59 0.151
## m6 -0.01259 NB(1.7382) 1.74 4 -229.942 469.5 3.30 0.106
## m1 NB(1.6841) 1.68 4 -230.482 470.6 4.38 0.062
## m5 NB(1.6552) 1.66 4 -230.780 471.2 4.98 0.046
## m3 NB(1.6542) 1.65 4 -230.790 471.2 5.00 0.045
## m4 NB(1.6451) 1.65 4 -230.885 471.4 5.19 0.041
## Abbreviations:
## family: NB(1.6398) = 'Negative Binomial(1.6398)',
## NB(1.6451) = 'Negative Binomial(1.6451)',
## NB(1.6542) = 'Negative Binomial(1.6542)',
## NB(1.6552) = 'Negative Binomial(1.6552)',
## NB(1.6841) = 'Negative Binomial(1.6841)',
## NB(1.7382) = 'Negative Binomial(1.7382)',
## NB(1.916) = 'Negative Binomial(1.916)'
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m2 1.3510 0.9933 2.585
## m6 1.0560 1.1950
## m1 3.4220 -0.005775 0.7659
## m5 2.3600 0.9246 -0.06198
## m3 2.5880 0.8774 -0.3827
## m4 0.9766 1.2000 -0.006725
## lst_wet df logLik AICc delta weight
## m2 3 -228.292 463.5 0.00 0.681
## m6 -0.01259 3 -230.028 467.0 3.47 0.120
## m1 3 -230.635 468.2 4.69 0.065
## m5 3 -230.977 468.9 5.37 0.046
## m3 3 -230.988 468.9 5.39 0.046
## m4 3 -231.100 469.2 5.62 0.041
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) cv.dpt prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m0 0.3052 1.0160
## m2 0.1334 0.9884 3.407
## m1 1.4070 0.8794 -0.009381
## m4 0.5656 0.9220 0.005582
## m6 0.4435 1.0030
## m3 0.4404 0.9946 -0.6404
## m5 0.3143 1.0140 -0.002421
## lst_wet family init.theta df logLik AICc delta weight
## m0 NB(1.2208) 1.22 3 -208.540 424.0 0.00 0.347
## m2 NB(1.255) 1.26 4 -208.048 425.8 1.72 0.147
## m1 NB(1.2461) 1.25 4 -208.176 426.0 1.98 0.129
## m4 NB(1.2277) 1.23 4 -208.440 426.5 2.51 0.099
## m6 -0.004299 NB(1.2253) 1.23 4 -208.475 426.6 2.58 0.096
## m3 NB(1.223) 1.22 4 -208.509 426.7 2.64 0.093
## m5 NB(1.2208) 1.22 4 -208.540 426.7 2.71 0.090
## Abbreviations:
## family: NB(1.2208) = 'Negative Binomial(1.2208)',
## NB(1.223) = 'Negative Binomial(1.223)',
## NB(1.2253) = 'Negative Binomial(1.2253)',
## NB(1.2277) = 'Negative Binomial(1.2277)',
## NB(1.2461) = 'Negative Binomial(1.2461)',
## NB(1.255) = 'Negative Binomial(1.255)'
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m2 0.1334 0.9884 3.407
## m1 1.4060 -0.009381 0.8794
## m4 0.5657 0.9219 0.005583
## m6 0.4436 1.0030
## m3 0.4404 0.9946 -0.6403
## m5 0.3146 1.0140 -0.002424
## lst_wet df logLik AICc delta weight
## m2 3 -208.055 423.1 0.00 0.224
## m1 3 -208.180 423.3 0.25 0.197
## m4 3 -208.440 423.8 0.77 0.152
## m6 -0.0043 3 -208.475 423.9 0.84 0.147
## m3 3 -208.509 424.0 0.91 0.142
## m5 3 -208.540 424.0 0.97 0.138
## Models ranked by AICc(x)
## Error in nrow(concord.out): object 'concord.out' not found
sites<-c("macae", "frenchguiana", "argentina")
concord.out6<-data.frame(Response=numeric(), Reference= numeric(), Target =numeric(), Model=numeric(), Sites=numeric(), CCC=numeric(),Precision=numeric(),Accuracy=numeric(), Kendall=numeric(), Spearman=numeric())
concord.out6<-concord.magic(sites, "piercer_bio", nococrprdata, 100, 2, "nb")#all but one model works
## Warning in matrix(value, n, p): data length [11] is not a sub-multiple or
## multiple of the number of columns [10]
## Warning in matrix(value, n, p): data length [11] is not a sub-multiple or
## multiple of the number of columns [10]
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning: glm.fit: fitted rates numerically 0 occurred
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: fitted rates numerically 0 occurred
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning: glm.fit: fitted rates numerically 0 occurred
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: fitted rates numerically 0 occurred
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: fitted rates numerically 0 occurred
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m3 895.600 -172.0000 55.04000 -104.50
## m0 8.494 -2.2330
## m2 -19.050 -0.5257 28.140
## m1 11.660 -2.8480 0.86160
## m4 -19.430 -0.5257 14.330 20.71
## m9 -21.100 -0.1458 0.04575 28.090
## m11 590.400 -115.8000 28.98000 6.849 -55.48
## m10 -21.500 -0.1458 0.01725 14.340 20.67
## m12 503.500 -109.0000 45.93000 6.672 28.71 105.00
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m3
## m0
## m2
## m1
## m4
## m9 1.0580
## m11 62.9600
## m10 0.5642 0.7714
## m12 64.2100 -31.8800
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 family
## m3 NB(21929.63)
## m0 NB(930.9829)
## m2 NB(5131.718)
## m1 NB(1475.133)
## m4 NB(5131.707)
## m9 NB(6752.342)
## m11 -78.17 NB(21929.64)
## m10 NB(6752.32)
## m12 -78.77 -176.5 NB(21929.63)
## init.theta df logLik AICc delta weight
## m3 21900 5 -1.000 14.5 0.00 0.407
## m0 931 3 -4.197 15.3 0.82 0.270
## m2 5130 4 -3.298 16.2 1.70 0.174
## m1 1480 4 -3.906 17.4 2.91 0.095
## m4 5130 5 -3.298 19.1 4.60 0.041
## m9 6750 6 -3.076 21.8 7.30 0.011
## m11 21900 8 -1.000 24.9 10.36 0.002
## m10 6750 8 -3.076 29.0 14.51 0.000
## m12 21900 11 -1.000 38.7 24.17 0.000
## Abbreviations:
## family: NB(1475.133) = 'Negative Binomial(1475.133)',
## NB(21929.63) = 'Negative Binomial(21929.63)',
## NB(21929.64) = 'Negative Binomial(21929.64)',
## NB(5131.707) = 'Negative Binomial(5131.707)',
## NB(5131.718) = 'Negative Binomial(5131.718)',
## NB(6752.32) = 'Negative Binomial(6752.32)',
## NB(6752.342) = 'Negative Binomial(6752.342)',
## NB(930.9829) = 'Negative Binomial(930.9829)'
## Models ranked by AICc(x)
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m3 789.200 -151.6000 49.45000 -93.24
## m0 8.494 -2.2330
## m2 -17.580 -0.5257 26.020
## m1 11.660 -2.8480 0.86160
## m4 -18.920 -0.5257 13.970 20.18
## m9 -20.630 -0.1458 0.02824 27.420
## m11 526.800 -103.4000 26.04000 6.329 -49.82
## m10 -20.990 -0.1458 0.01760 13.970 20.14
## m12 415.400 -90.4000 37.99000 5.869 23.69 87.67
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m3
## m0
## m2
## m1
## m4
## m9 1.0830
## m11 56.5000
## m10 0.5644 0.7704
## m12 58.4500 -18.7600
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik AICc
## m3 4 -1.000 11.6
## m0 2 -4.197 12.8
## m2 3 -3.298 13.5
## m1 3 -3.906 14.7
## m4 4 -3.298 16.2
## m9 5 -3.076 18.7
## m11 -70.13 7 -1.000 21.1
## m10 7 -3.076 25.2
## m12 -71.06 -154.6 10 -1.000 33.6
## delta weight
## m3 0.00 0.441
## m0 1.24 0.237
## m2 1.92 0.169
## m1 3.13 0.092
## m4 4.59 0.044
## m9 7.05 0.013
## m11 9.49 0.004
## m10 13.64 0.000
## m12 21.98 0.000
## Models ranked by AICc(x)
## Warning in cor(predobs, use = "pairwise.complete.obs", method =
## "spearman"): the standard deviation is zero
## Warning in cor(predobs, use = "pairwise.complete.obs", method =
## "spearman"): the standard deviation is zero
## Warning in matrix(value, n, p): data length [11] is not a sub-multiple or
## multiple of the number of columns [10]
## Warning in matrix(value, n, p): data length [11] is not a sub-multiple or
## multiple of the number of columns [10]
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Model selection table
## (Int) log(mxv) cv.dpt prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m0 8.4940 -2.2330
## m2 18.4900 -3.5580 -23.79
## m1 0.8721 -1.0780 0.0445
## m4 3.2700 -0.7209 -0.06646
## m6 7.9430 -2.1760
## m3 10.4000 -2.5630 -7.865
## m5 7.7100 -2.1120 0.09109
## lst_wet family init.theta df logLik AICc delta weight
## m0 NB(930.9829) 931 3 -4.197 15.3 0.00 0.306
## m2 NB(5735.135) 5740 4 -3.121 15.8 0.53 0.235
## m1 NB(1290.719) 1290 4 -3.943 17.5 2.17 0.104
## m4 NB(1375.735) 1380 4 -3.957 17.5 2.20 0.102
## m6 0.03569 NB(1035.374) 1040 4 -4.129 17.9 2.54 0.086
## m3 NB(991.7642) 992 4 -4.154 17.9 2.59 0.084
## m5 NB(979.0643) 979 4 -4.167 17.9 2.62 0.083
## Abbreviations:
## family: NB(1035.374) = 'Negative Binomial(1035.374)',
## NB(1290.719) = 'Negative Binomial(1290.719)',
## NB(1375.735) = 'Negative Binomial(1375.735)',
## NB(5735.135) = 'Negative Binomial(5735.135)',
## NB(930.9829) = 'Negative Binomial(930.9829)',
## NB(979.0643) = 'Negative Binomial(979.0643)',
## NB(991.7642) = 'Negative Binomial(991.7642)'
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m2 18.5000 -3.5590 -23.79
## m1 0.8721 0.0445 -1.0780
## m4 3.2700 -0.7209 -0.06647
## m6 7.9430 -2.1760
## m3 10.4000 -2.5630 -7.865
## m5 7.7100 -2.1120 0.09109
## lst_wet df logLik AICc delta weight
## m2 3 -3.121 13.2 0.00 0.339
## m1 3 -3.943 14.8 1.64 0.149
## m4 3 -3.957 14.8 1.67 0.147
## m6 0.03569 3 -4.129 15.2 2.01 0.124
## m3 3 -4.154 15.2 2.07 0.121
## m5 3 -4.167 15.3 2.09 0.119
## Models ranked by AICc(x)
## Warning in cor(predobs, use = "pairwise.complete.obs", method =
## "spearman"): the standard deviation is zero
## Warning in cor(predobs, use = "pairwise.complete.obs", method =
## "spearman"): the standard deviation is zero
## Error in nrow(concord.out): object 'concord.out' not found
sites<-c("puertorico", "macae", "frenchguiana", "costarica")
noargco123cleandata<-filter(noargco123data, bacteria.per.nl.final%nin%NA)#original fails as puertorico has NA values
concord.out7<-concord.magic(sites, "bacteria.per.nl.final", noargco123cleandata, 100, 2, "nb")#all models work!
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m4 5.266 0.4432 0.1522 0.9207
## m0 5.461 0.4622
## m2 4.900 0.5543 0.2023
## m1 5.972 0.3726 -0.058990
## m10 5.808 0.3521 0.050350 0.1956 0.8576
## m9 6.301 0.3120 -0.099600 0.2425
## m3 5.561 0.4339 -0.007765 0.05413
## m11 5.859 0.3760 -0.032180 0.2237 0.07083
## m12 6.591 0.2337 -0.076300 0.2958 -0.11130 0.2443
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m4
## m0
## m2
## m1
## m10 0.3009 -0.46070
## m9 0.2416
## m3
## m11 0.2876
## m12 0.3151 0.07922
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 family
## m4 NB(6.5101)
## m0 NB(4.8222)
## m2 NB(5.1207)
## m1 NB(4.9033)
## m10 NB(8.5744)
## m9 NB(5.8386)
## m3 NB(4.9749)
## m11 0.02069 NB(6.0479)
## m12 -0.11950 0.6715 NB(10.9094)
## init.theta df logLik AICc delta weight
## m4 6.51 5 -214.125 441.4 0.00 0.535
## m0 4.82 3 -218.092 443.3 1.92 0.205
## m2 5.12 4 -217.292 444.6 3.18 0.109
## m1 4.9 4 -217.869 445.7 4.33 0.061
## m10 8.57 8 -210.540 446.1 4.67 0.052
## m9 5.84 6 -215.557 447.8 6.37 0.022
## m3 4.97 5 -217.677 448.5 7.10 0.015
## m11 6.05 8 -215.093 455.2 13.78 0.001
## m12 10.9 11 -207.438 457.2 15.78 0.000
## Abbreviations:
## family: NB(10.9094) = 'Negative Binomial(10.9094)',
## NB(4.8222) = 'Negative Binomial(4.8222)',
## NB(4.9033) = 'Negative Binomial(4.9033)',
## NB(4.9749) = 'Negative Binomial(4.9749)',
## NB(5.1207) = 'Negative Binomial(5.1207)',
## NB(5.8386) = 'Negative Binomial(5.8386)',
## NB(6.0479) = 'Negative Binomial(6.0479)',
## NB(6.5101) = 'Negative Binomial(6.5101)',
## NB(8.5744) = 'Negative Binomial(8.5744)'
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m4 5.266 0.4431 0.1522 0.9206
## m0 5.462 0.4622
## m2 4.900 0.5543 0.2024
## m10 5.808 0.3521 0.05037 0.1956 0.8576
## m1 5.972 0.3725 -0.05900
## m9 6.301 0.3120 -0.09960 0.2425
## m3 5.561 0.4338 -0.00776 0.05413
## m11 5.859 0.3761 -0.03217 0.2237 0.07083
## m12 6.591 0.2337 -0.07626 0.2959 -0.11130 0.2443
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m4
## m0
## m2
## m10 0.3009 -0.46060
## m1
## m9 0.2416
## m3
## m11 0.2876
## m12 0.3151 0.07915
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik
## m4 4 -214.125
## m0 2 -218.752
## m2 3 -217.705
## m10 7 -210.990
## m1 3 -218.455
## m9 5 -215.638
## m3 4 -218.200
## m11 0.02071 7 -215.129
## m12 -0.11950 0.6715 10 -208.894
## AICc delta weight
## m4 438.2 0.00 0.666
## m0 442.0 3.80 0.100
## m2 442.6 4.30 0.077
## m10 442.6 4.32 0.077
## m1 444.1 5.80 0.037
## m9 444.4 6.18 0.030
## m3 446.4 8.15 0.011
## m11 450.8 12.60 0.001
## m12 453.5 15.25 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m4 5.266 0.4432 0.1522 0.9207
## m0 5.461 0.4622
## m2 4.900 0.5543 0.2023
## m1 5.972 0.3726 -0.058990
## m10 5.808 0.3521 0.050350 0.1956 0.8576
## m9 6.301 0.3120 -0.099600 0.2425
## m3 5.561 0.4339 -0.007765 0.05413
## m11 5.859 0.3760 -0.032180 0.2237 0.07083
## m12 6.591 0.2337 -0.076300 0.2958 -0.11130 0.2443
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m4
## m0
## m2
## m1
## m10 0.3009 -0.46070
## m9 0.2416
## m3
## m11 0.2876
## m12 0.3151 0.07922
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 family
## m4 NB(6.5101)
## m0 NB(4.8222)
## m2 NB(5.1207)
## m1 NB(4.9033)
## m10 NB(8.5744)
## m9 NB(5.8386)
## m3 NB(4.9749)
## m11 0.02069 NB(6.0479)
## m12 -0.11950 0.6715 NB(10.9094)
## init.theta df logLik AICc delta weight
## m4 6.51 5 -214.125 441.4 0.00 0.535
## m0 4.82 3 -218.092 443.3 1.92 0.205
## m2 5.12 4 -217.292 444.6 3.18 0.109
## m1 4.9 4 -217.869 445.7 4.33 0.061
## m10 8.57 8 -210.540 446.1 4.67 0.052
## m9 5.84 6 -215.557 447.8 6.37 0.022
## m3 4.97 5 -217.677 448.5 7.10 0.015
## m11 6.05 8 -215.093 455.2 13.78 0.001
## m12 10.9 11 -207.438 457.2 15.78 0.000
## Abbreviations:
## family: NB(10.9094) = 'Negative Binomial(10.9094)',
## NB(4.8222) = 'Negative Binomial(4.8222)',
## NB(4.9033) = 'Negative Binomial(4.9033)',
## NB(4.9749) = 'Negative Binomial(4.9749)',
## NB(5.1207) = 'Negative Binomial(5.1207)',
## NB(5.8386) = 'Negative Binomial(5.8386)',
## NB(6.0479) = 'Negative Binomial(6.0479)',
## NB(6.5101) = 'Negative Binomial(6.5101)',
## NB(8.5744) = 'Negative Binomial(8.5744)'
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m4 5.266 0.4431 0.1522 0.9206
## m0 5.462 0.4622
## m2 4.900 0.5543 0.2024
## m10 5.808 0.3521 0.05037 0.1956 0.8576
## m1 5.972 0.3725 -0.05900
## m9 6.301 0.3120 -0.09960 0.2425
## m3 5.561 0.4338 -0.00776 0.05413
## m11 5.859 0.3761 -0.03217 0.2237 0.07083
## m12 6.591 0.2337 -0.07626 0.2959 -0.11130 0.2443
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m4
## m0
## m2
## m10 0.3009 -0.46060
## m1
## m9 0.2416
## m3
## m11 0.2876
## m12 0.3151 0.07915
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik
## m4 4 -214.125
## m0 2 -218.752
## m2 3 -217.705
## m10 7 -210.990
## m1 3 -218.455
## m9 5 -215.638
## m3 4 -218.200
## m11 0.02071 7 -215.129
## m12 -0.11950 0.6715 10 -208.894
## AICc delta weight
## m4 438.2 0.00 0.666
## m0 442.0 3.80 0.100
## m2 442.6 4.30 0.077
## m10 442.6 4.32 0.077
## m1 444.1 5.80 0.037
## m9 444.4 6.18 0.030
## m3 446.4 8.15 0.011
## m11 450.8 12.60 0.001
## m12 453.5 15.25 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m4 5.266 0.4432 0.1522 0.9207
## m0 5.461 0.4622
## m2 4.900 0.5543 0.2023
## m1 5.972 0.3726 -0.058990
## m10 5.808 0.3521 0.050350 0.1956 0.8576
## m9 6.301 0.3120 -0.099600 0.2425
## m3 5.561 0.4339 -0.007765 0.05413
## m11 5.859 0.3760 -0.032180 0.2237 0.07083
## m12 6.591 0.2337 -0.076300 0.2958 -0.11130 0.2443
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m4
## m0
## m2
## m1
## m10 0.3009 -0.46070
## m9 0.2416
## m3
## m11 0.2876
## m12 0.3151 0.07922
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 family
## m4 NB(6.5101)
## m0 NB(4.8222)
## m2 NB(5.1207)
## m1 NB(4.9033)
## m10 NB(8.5744)
## m9 NB(5.8386)
## m3 NB(4.9749)
## m11 0.02069 NB(6.0479)
## m12 -0.11950 0.6715 NB(10.9094)
## init.theta df logLik AICc delta weight
## m4 6.51 5 -214.125 441.4 0.00 0.535
## m0 4.82 3 -218.092 443.3 1.92 0.205
## m2 5.12 4 -217.292 444.6 3.18 0.109
## m1 4.9 4 -217.869 445.7 4.33 0.061
## m10 8.57 8 -210.540 446.1 4.67 0.052
## m9 5.84 6 -215.557 447.8 6.37 0.022
## m3 4.97 5 -217.677 448.5 7.10 0.015
## m11 6.05 8 -215.093 455.2 13.78 0.001
## m12 10.9 11 -207.438 457.2 15.78 0.000
## Abbreviations:
## family: NB(10.9094) = 'Negative Binomial(10.9094)',
## NB(4.8222) = 'Negative Binomial(4.8222)',
## NB(4.9033) = 'Negative Binomial(4.9033)',
## NB(4.9749) = 'Negative Binomial(4.9749)',
## NB(5.1207) = 'Negative Binomial(5.1207)',
## NB(5.8386) = 'Negative Binomial(5.8386)',
## NB(6.0479) = 'Negative Binomial(6.0479)',
## NB(6.5101) = 'Negative Binomial(6.5101)',
## NB(8.5744) = 'Negative Binomial(8.5744)'
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m4 5.266 0.4431 0.1522 0.9206
## m0 5.462 0.4622
## m2 4.900 0.5543 0.2024
## m10 5.808 0.3521 0.05037 0.1956 0.8576
## m1 5.972 0.3725 -0.05900
## m9 6.301 0.3120 -0.09960 0.2425
## m3 5.561 0.4338 -0.00776 0.05413
## m11 5.859 0.3761 -0.03217 0.2237 0.07083
## m12 6.591 0.2337 -0.07626 0.2959 -0.11130 0.2443
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m4
## m0
## m2
## m10 0.3009 -0.46060
## m1
## m9 0.2416
## m3
## m11 0.2876
## m12 0.3151 0.07915
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik
## m4 4 -214.125
## m0 2 -218.752
## m2 3 -217.705
## m10 7 -210.990
## m1 3 -218.455
## m9 5 -215.638
## m3 4 -218.200
## m11 0.02071 7 -215.129
## m12 -0.11950 0.6715 10 -208.894
## AICc delta weight
## m4 438.2 0.00 0.666
## m0 442.0 3.80 0.100
## m2 442.6 4.30 0.077
## m10 442.6 4.32 0.077
## m1 444.1 5.80 0.037
## m9 444.4 6.18 0.030
## m3 446.4 8.15 0.011
## m11 450.8 12.60 0.001
## m12 453.5 15.25 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 12.56 -0.8024
## m2 13.66 -0.9624 0.121700
## m1 13.11 -0.8818 0.02985
## m4 15.23 -1.1760 0.133600 -0.2875
## m3 12.93 -0.8586 0.05361 0.02342
## m9 14.29 -1.0530 0.03633 0.123100
## m11 13.71 -0.9706 0.05002 -0.016240 0.01199
## m10 16.14 -1.3060 0.03118 0.138000 -0.3013
## m12 16.09 -1.2970 0.01395 0.007074 -0.01555 -0.3778
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m2
## m1
## m4
## m3
## m9 -0.01787
## m11 0.12430
## m10 -0.01445 0.04035
## m12 0.12520 0.12960
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 family
## m0 NB(12.2786)
## m2 NB(12.9366)
## m1 NB(12.4162)
## m4 NB(13.5835)
## m3 NB(12.5074)
## m9 NB(13.174)
## m11 0.1421 NB(14.4824)
## m10 NB(13.9736)
## m12 0.1372 0.07574 NB(15.5194)
## init.theta df logLik AICc delta weight
## m0 12.3 3 -214.853 436.6 0.00 0.435
## m2 12.9 4 -214.061 437.7 1.09 0.252
## m1 12.4 4 -214.682 439.0 2.34 0.135
## m4 13.6 5 -213.321 439.1 2.51 0.124
## m3 12.5 5 -214.571 441.6 5.01 0.035
## m9 13.2 6 -213.783 443.2 6.59 0.016
## m11 14.5 8 -212.346 447.5 10.92 0.002
## m10 14 8 -212.889 448.6 12.01 0.001
## m12 15.5 11 -211.299 459.3 22.64 0.000
## Abbreviations:
## family: NB(12.2786) = 'Negative Binomial(12.2786)',
## NB(12.4162) = 'Negative Binomial(12.4162)',
## NB(12.5074) = 'Negative Binomial(12.5074)',
## NB(12.9366) = 'Negative Binomial(12.9366)',
## NB(13.174) = 'Negative Binomial(13.174)',
## NB(13.5835) = 'Negative Binomial(13.5835)',
## NB(13.9736) = 'Negative Binomial(13.9736)',
## NB(14.4824) = 'Negative Binomial(14.4824)',
## NB(15.5194) = 'Negative Binomial(15.5194)'
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 12.56 -0.8024
## m2 13.66 -0.9624 0.121700
## m1 13.11 -0.8818 0.02985
## m4 15.23 -1.1760 0.133600 -0.2876
## m3 12.93 -0.8586 0.05362 0.02342
## m9 14.29 -1.0530 0.03634 0.123100
## m11 13.71 -0.9708 0.05004 -0.016290 0.01199
## m10 16.14 -1.3070 0.03121 0.138000 -0.3014
## m12 16.09 -1.2980 0.01403 0.007025 -0.01554 -0.3780
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m2
## m1
## m4
## m3
## m9 -0.01788
## m11 0.12430
## m10 -0.01445 0.04033
## m12 0.12520 0.12950
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik
## m0 2 -214.853
## m2 3 -214.080
## m1 3 -214.683
## m4 4 -213.392
## m3 4 -214.573
## m9 5 -213.818
## m11 0.1421 7 -212.536
## m10 7 -213.006
## m12 0.1373 0.07571 10 -211.674
## AICc delta weight
## m0 434.2 0.00 0.407
## m2 435.1 0.93 0.255
## m1 436.3 2.14 0.140
## m4 436.4 2.23 0.133
## m3 438.7 4.60 0.041
## m9 440.1 5.99 0.020
## m11 444.2 10.01 0.003
## m10 445.1 10.95 0.002
## m12 454.9 20.78 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 12.56 -0.8024
## m2 13.66 -0.9624 0.121700
## m1 13.11 -0.8818 0.02985
## m4 15.23 -1.1760 0.133600 -0.2875
## m3 12.93 -0.8586 0.05361 0.02342
## m9 14.29 -1.0530 0.03633 0.123100
## m11 13.71 -0.9706 0.05002 -0.016240 0.01199
## m10 16.14 -1.3060 0.03118 0.138000 -0.3013
## m12 16.09 -1.2970 0.01395 0.007074 -0.01555 -0.3778
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m2
## m1
## m4
## m3
## m9 -0.01787
## m11 0.12430
## m10 -0.01445 0.04035
## m12 0.12520 0.12960
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 family
## m0 NB(12.2786)
## m2 NB(12.9366)
## m1 NB(12.4162)
## m4 NB(13.5835)
## m3 NB(12.5074)
## m9 NB(13.174)
## m11 0.1421 NB(14.4824)
## m10 NB(13.9736)
## m12 0.1372 0.07574 NB(15.5194)
## init.theta df logLik AICc delta weight
## m0 12.3 3 -214.853 436.6 0.00 0.435
## m2 12.9 4 -214.061 437.7 1.09 0.252
## m1 12.4 4 -214.682 439.0 2.34 0.135
## m4 13.6 5 -213.321 439.1 2.51 0.124
## m3 12.5 5 -214.571 441.6 5.01 0.035
## m9 13.2 6 -213.783 443.2 6.59 0.016
## m11 14.5 8 -212.346 447.5 10.92 0.002
## m10 14 8 -212.889 448.6 12.01 0.001
## m12 15.5 11 -211.299 459.3 22.64 0.000
## Abbreviations:
## family: NB(12.2786) = 'Negative Binomial(12.2786)',
## NB(12.4162) = 'Negative Binomial(12.4162)',
## NB(12.5074) = 'Negative Binomial(12.5074)',
## NB(12.9366) = 'Negative Binomial(12.9366)',
## NB(13.174) = 'Negative Binomial(13.174)',
## NB(13.5835) = 'Negative Binomial(13.5835)',
## NB(13.9736) = 'Negative Binomial(13.9736)',
## NB(14.4824) = 'Negative Binomial(14.4824)',
## NB(15.5194) = 'Negative Binomial(15.5194)'
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 12.56 -0.8024
## m2 13.66 -0.9624 0.121700
## m1 13.11 -0.8818 0.02985
## m4 15.23 -1.1760 0.133600 -0.2876
## m3 12.93 -0.8586 0.05362 0.02342
## m9 14.29 -1.0530 0.03634 0.123100
## m11 13.71 -0.9708 0.05004 -0.016290 0.01199
## m10 16.14 -1.3070 0.03121 0.138000 -0.3014
## m12 16.09 -1.2980 0.01403 0.007025 -0.01554 -0.3780
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m2
## m1
## m4
## m3
## m9 -0.01788
## m11 0.12430
## m10 -0.01445 0.04033
## m12 0.12520 0.12950
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik
## m0 2 -214.853
## m2 3 -214.080
## m1 3 -214.683
## m4 4 -213.392
## m3 4 -214.573
## m9 5 -213.818
## m11 0.1421 7 -212.536
## m10 7 -213.006
## m12 0.1373 0.07571 10 -211.674
## AICc delta weight
## m0 434.2 0.00 0.407
## m2 435.1 0.93 0.255
## m1 436.3 2.14 0.140
## m4 436.4 2.23 0.133
## m3 438.7 4.60 0.041
## m9 440.1 5.99 0.020
## m11 444.2 10.01 0.003
## m10 445.1 10.95 0.002
## m12 454.9 20.78 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m1 3.7200 0.56830 -0.3494
## m3 4.3790 0.47030 -0.4765 -0.1447
## m0 5.1090 0.33860
## m4 2.1220 0.79770 -0.03873 1.4360
## m9 4.8220 0.36430 -0.3409 -0.18400
## m2 6.4340 0.09288 -0.19680
## m10 0.4641 1.07600 -0.2854 -0.02725 1.4410
## m11 5.1180 0.33170 -0.4771 -0.38020 -0.1463
## m12 0.7359 1.07800 -0.7779 -0.24470 -0.3898 0.9681
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m1
## m3
## m0
## m4
## m9 -0.05436
## m2
## m10 -0.05650 -0.1884
## m11 0.13660
## m12 0.12730 0.7123
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 family
## m1 NB(2.1658)
## m3 NB(2.2526)
## m0 NB(1.7705)
## m4 NB(2.0283)
## m9 NB(2.2033)
## m2 NB(1.7989)
## m10 NB(2.608)
## m11 0.2160 NB(2.3775)
## m12 0.2381 0.6675 NB(3.0375)
## init.theta df logLik AICc delta weight
## m1 2.17 4 -232.293 474.2 0.00 0.558
## m3 2.25 5 -231.622 475.7 1.56 0.256
## m0 1.77 3 -235.796 478.5 4.33 0.064
## m4 2.03 5 -233.421 479.3 5.16 0.042
## m9 2.2 6 -231.999 479.7 5.46 0.036
## m2 1.8 4 -235.516 480.6 6.45 0.022
## m10 2.61 8 -229.136 481.1 6.94 0.017
## m11 2.38 8 -230.703 484.3 10.08 0.004
## m12 3.04 11 -226.590 489.8 15.66 0.000
## Abbreviations:
## family: NB(1.7705) = 'Negative Binomial(1.7705)',
## NB(1.7989) = 'Negative Binomial(1.7989)',
## NB(2.0283) = 'Negative Binomial(2.0283)',
## NB(2.1658) = 'Negative Binomial(2.1658)',
## NB(2.2033) = 'Negative Binomial(2.2033)',
## NB(2.2526) = 'Negative Binomial(2.2526)',
## NB(2.3775) = 'Negative Binomial(2.3775)',
## NB(2.608) = 'Negative Binomial(2.608)',
## NB(3.0375) = 'Negative Binomial(3.0375)'
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m1 3.7200 0.5682 -0.3494
## m3 4.3790 0.4703 -0.4765 -0.1447
## m9 4.8220 0.3643 -0.3409 -0.18400
## m4 2.1220 0.7976 -0.03875 1.4360
## m0 5.1090 0.3385
## m10 0.4606 1.0770 -0.2855 -0.02707 1.4410
## m2 6.4340 0.0929 -0.19690
## m11 5.1180 0.3316 -0.4771 -0.38020 -0.1463
## m12 0.7300 1.0790 -0.7784 -0.24450 -0.3900 0.9687
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m1
## m3
## m9 -0.05435
## m4
## m0
## m10 -0.05647 -0.1882
## m2
## m11 0.13670
## m12 0.12730 0.7137
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik
## m1 3 -232.293
## m3 4 -231.633
## m9 5 -232.001
## m4 4 -233.461
## m0 2 -236.182
## m10 7 -229.404
## m2 3 -235.842
## m11 0.2160 7 -230.772
## m12 0.2382 0.6682 10 -227.436
## AICc delta weight
## m1 471.5 0.00 0.549
## m3 472.9 1.36 0.278
## m9 476.5 4.99 0.045
## m4 476.5 5.01 0.045
## m0 476.8 5.30 0.039
## m10 477.9 6.39 0.022
## m2 478.6 7.10 0.016
## m11 480.6 9.13 0.006
## m12 486.5 14.94 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) cv.dpt prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m0 5.461 0.4622
## m4 4.817 0.6269 -0.008498
## m6 4.965 0.5330
## m5 5.587 0.4439 -0.00581
## m1 5.578 0.4482 -0.0007859
## m2 5.524 0.4457 0.1645
## m3 5.399 0.4714 0.08801
## lst_wet family init.theta df logLik AICc delta weight
## m0 NB(4.8222) 4.82 3 -218.092 443.3 0.00 0.356
## m4 NB(5.0899) 5.09 4 -217.372 444.7 1.42 0.175
## m6 0.007097 NB(4.9552) 4.96 4 -217.729 445.5 2.13 0.123
## m5 NB(4.8317) 4.83 4 -218.065 446.1 2.80 0.088
## m1 NB(4.8289) 4.83 4 -218.073 446.1 2.82 0.087
## m2 NB(4.827) 4.83 4 -218.078 446.2 2.83 0.086
## m3 NB(4.8245) 4.82 4 -218.086 446.2 2.84 0.086
## Abbreviations:
## family: NB(4.8222) = 'Negative Binomial(4.8222)',
## NB(4.8245) = 'Negative Binomial(4.8245)',
## NB(4.827) = 'Negative Binomial(4.827)',
## NB(4.8289) = 'Negative Binomial(4.8289)',
## NB(4.8317) = 'Negative Binomial(4.8317)',
## NB(4.9552) = 'Negative Binomial(4.9552)',
## NB(5.0899) = 'Negative Binomial(5.0899)'
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m4 4.817 0.6269 -0.008498
## m6 4.965 0.5329
## m5 5.587 0.4439 -0.00581
## m1 5.578 -0.0007859 0.4482
## m2 5.525 0.4457 0.1645
## m3 5.399 0.4714 0.08802
## lst_wet df logLik AICc delta weight
## m4 3 -217.392 441.9 0.00 0.268
## m6 0.007097 3 -217.734 442.6 0.68 0.190
## m5 3 -218.065 443.3 1.35 0.137
## m1 3 -218.073 443.3 1.36 0.136
## m2 3 -218.078 443.3 1.37 0.135
## m3 3 -218.086 443.3 1.39 0.134
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) cv.dpt prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m0 5.461 0.4622
## m4 4.817 0.6269 -0.008498
## m6 4.965 0.5330
## m5 5.587 0.4439 -0.00581
## m1 5.578 0.4482 -0.0007859
## m2 5.524 0.4457 0.1645
## m3 5.399 0.4714 0.08801
## lst_wet family init.theta df logLik AICc delta weight
## m0 NB(4.8222) 4.82 3 -218.092 443.3 0.00 0.356
## m4 NB(5.0899) 5.09 4 -217.372 444.7 1.42 0.175
## m6 0.007097 NB(4.9552) 4.96 4 -217.729 445.5 2.13 0.123
## m5 NB(4.8317) 4.83 4 -218.065 446.1 2.80 0.088
## m1 NB(4.8289) 4.83 4 -218.073 446.1 2.82 0.087
## m2 NB(4.827) 4.83 4 -218.078 446.2 2.83 0.086
## m3 NB(4.8245) 4.82 4 -218.086 446.2 2.84 0.086
## Abbreviations:
## family: NB(4.8222) = 'Negative Binomial(4.8222)',
## NB(4.8245) = 'Negative Binomial(4.8245)',
## NB(4.827) = 'Negative Binomial(4.827)',
## NB(4.8289) = 'Negative Binomial(4.8289)',
## NB(4.8317) = 'Negative Binomial(4.8317)',
## NB(4.9552) = 'Negative Binomial(4.9552)',
## NB(5.0899) = 'Negative Binomial(5.0899)'
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m4 4.817 0.6269 -0.008498
## m6 4.965 0.5329
## m5 5.587 0.4439 -0.00581
## m1 5.578 -0.0007859 0.4482
## m2 5.525 0.4457 0.1645
## m3 5.399 0.4714 0.08802
## lst_wet df logLik AICc delta weight
## m4 3 -217.392 441.9 0.00 0.268
## m6 0.007097 3 -217.734 442.6 0.68 0.190
## m5 3 -218.065 443.3 1.35 0.137
## m1 3 -218.073 443.3 1.36 0.136
## m2 3 -218.078 443.3 1.37 0.135
## m3 3 -218.086 443.3 1.39 0.134
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) cv.dpt prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m0 5.461 0.4622
## m4 4.817 0.6269 -0.008498
## m6 4.965 0.5330
## m5 5.587 0.4439 -0.00581
## m1 5.578 0.4482 -0.0007859
## m2 5.524 0.4457 0.1645
## m3 5.399 0.4714 0.08801
## lst_wet family init.theta df logLik AICc delta weight
## m0 NB(4.8222) 4.82 3 -218.092 443.3 0.00 0.356
## m4 NB(5.0899) 5.09 4 -217.372 444.7 1.42 0.175
## m6 0.007097 NB(4.9552) 4.96 4 -217.729 445.5 2.13 0.123
## m5 NB(4.8317) 4.83 4 -218.065 446.1 2.80 0.088
## m1 NB(4.8289) 4.83 4 -218.073 446.1 2.82 0.087
## m2 NB(4.827) 4.83 4 -218.078 446.2 2.83 0.086
## m3 NB(4.8245) 4.82 4 -218.086 446.2 2.84 0.086
## Abbreviations:
## family: NB(4.8222) = 'Negative Binomial(4.8222)',
## NB(4.8245) = 'Negative Binomial(4.8245)',
## NB(4.827) = 'Negative Binomial(4.827)',
## NB(4.8289) = 'Negative Binomial(4.8289)',
## NB(4.8317) = 'Negative Binomial(4.8317)',
## NB(4.9552) = 'Negative Binomial(4.9552)',
## NB(5.0899) = 'Negative Binomial(5.0899)'
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m4 4.817 0.6269 -0.008498
## m6 4.965 0.5329
## m5 5.587 0.4439 -0.00581
## m1 5.578 -0.0007859 0.4482
## m2 5.525 0.4457 0.1645
## m3 5.399 0.4714 0.08802
## lst_wet df logLik AICc delta weight
## m4 3 -217.392 441.9 0.00 0.268
## m6 0.007097 3 -217.734 442.6 0.68 0.190
## m5 3 -218.065 443.3 1.35 0.137
## m1 3 -218.073 443.3 1.36 0.136
## m2 3 -218.078 443.3 1.37 0.135
## m3 3 -218.086 443.3 1.39 0.134
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) cv.dpt prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m1 12.15 -0.7255 -0.003324
## m0 12.56 -0.8024
## m3 12.16 -0.7420 -0.6107
## m5 11.98 -0.7150 -0.01509
## m4 12.53 -0.8210 0.002337
## m6 12.59 -0.8042
## m2 12.58 -0.8065 0.09136
## lst_wet family init.theta df logLik AICc delta weight
## m1 NB(14.0757) 14.1 4 -212.777 435.2 0.00 0.374
## m0 NB(12.2786) 12.3 3 -214.853 436.6 1.47 0.179
## m3 NB(13.3031) 13.3 4 -213.635 436.9 1.72 0.159
## m5 NB(13.0858) 13.1 4 -213.884 437.4 2.21 0.124
## m4 NB(12.5828) 12.6 4 -214.480 438.6 3.41 0.068
## m6 -0.0008433 NB(12.3249) 12.3 4 -214.795 439.2 4.04 0.050
## m2 NB(12.2806) 12.3 4 -214.850 439.3 4.15 0.047
## Abbreviations:
## family: NB(12.2786) = 'Negative Binomial(12.2786)',
## NB(12.2806) = 'Negative Binomial(12.2806)',
## NB(12.3249) = 'Negative Binomial(12.3249)',
## NB(12.5828) = 'Negative Binomial(12.5828)',
## NB(13.0858) = 'Negative Binomial(13.0858)',
## NB(13.3031) = 'Negative Binomial(13.3031)',
## NB(14.0757) = 'Negative Binomial(14.0757)'
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m1 12.15 -0.003324 -0.7255
## m3 12.16 -0.7420 -0.6107
## m5 11.98 -0.7151 -0.01509
## m4 12.53 -0.8210 0.002337
## m6 12.59 -0.8042
## m2 12.58 -0.8065 0.09114
## lst_wet df logLik AICc delta weight
## m1 3 -212.777 432.5 0.00 0.472
## m3 3 -213.661 434.2 1.77 0.195
## m5 3 -213.927 434.8 2.30 0.150
## m4 3 -214.581 436.1 3.61 0.078
## m6 -0.0008428 3 -214.937 436.8 4.32 0.054
## m2 3 -215.000 436.9 4.45 0.051
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) cv.dpt prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m1 12.15 -0.7255 -0.003324
## m0 12.56 -0.8024
## m3 12.16 -0.7420 -0.6107
## m5 11.98 -0.7150 -0.01509
## m4 12.53 -0.8210 0.002337
## m6 12.59 -0.8042
## m2 12.58 -0.8065 0.09136
## lst_wet family init.theta df logLik AICc delta weight
## m1 NB(14.0757) 14.1 4 -212.777 435.2 0.00 0.374
## m0 NB(12.2786) 12.3 3 -214.853 436.6 1.47 0.179
## m3 NB(13.3031) 13.3 4 -213.635 436.9 1.72 0.159
## m5 NB(13.0858) 13.1 4 -213.884 437.4 2.21 0.124
## m4 NB(12.5828) 12.6 4 -214.480 438.6 3.41 0.068
## m6 -0.0008433 NB(12.3249) 12.3 4 -214.795 439.2 4.04 0.050
## m2 NB(12.2806) 12.3 4 -214.850 439.3 4.15 0.047
## Abbreviations:
## family: NB(12.2786) = 'Negative Binomial(12.2786)',
## NB(12.2806) = 'Negative Binomial(12.2806)',
## NB(12.3249) = 'Negative Binomial(12.3249)',
## NB(12.5828) = 'Negative Binomial(12.5828)',
## NB(13.0858) = 'Negative Binomial(13.0858)',
## NB(13.3031) = 'Negative Binomial(13.3031)',
## NB(14.0757) = 'Negative Binomial(14.0757)'
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m1 12.15 -0.003324 -0.7255
## m3 12.16 -0.7420 -0.6107
## m5 11.98 -0.7151 -0.01509
## m4 12.53 -0.8210 0.002337
## m6 12.59 -0.8042
## m2 12.58 -0.8065 0.09114
## lst_wet df logLik AICc delta weight
## m1 3 -212.777 432.5 0.00 0.472
## m3 3 -213.661 434.2 1.77 0.195
## m5 3 -213.927 434.8 2.30 0.150
## m4 3 -214.581 436.1 3.61 0.078
## m6 -0.0008428 3 -214.937 436.8 4.32 0.054
## m2 3 -215.000 436.9 4.45 0.051
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) cv.dpt prp.ovr.dys prp.drd.dys men.dpt lng_dry lst_wet
## m3 3.028 0.6968 5.055
## m6 3.605 0.5855 0.02865
## m5 3.642 0.5870 0.09016
## m0 5.109 0.3386
## m4 4.098 0.6650 -0.01574
## m1 3.093 0.6555 0.01113
## m2 5.150 0.3401 -0.2013
## family init.theta df logLik AICc delta weight
## m3 NB(1.9901) 1.99 4 -233.754 477.1 0.00 0.277
## m6 NB(1.9534) 1.95 4 -234.077 477.8 0.65 0.200
## m5 NB(1.9326) 1.93 4 -234.263 478.1 1.02 0.166
## m0 NB(1.7705) 1.77 3 -235.796 478.5 1.41 0.137
## m4 NB(1.8753) 1.88 4 -234.788 479.2 2.07 0.098
## m1 NB(1.8597) 1.86 4 -234.934 479.5 2.36 0.085
## m2 NB(1.7735) 1.77 4 -235.767 481.1 4.03 0.037
## Abbreviations:
## family: NB(1.7705) = 'Negative Binomial(1.7705)',
## NB(1.7735) = 'Negative Binomial(1.7735)',
## NB(1.8597) = 'Negative Binomial(1.8597)',
## NB(1.8753) = 'Negative Binomial(1.8753)',
## NB(1.9326) = 'Negative Binomial(1.9326)',
## NB(1.9534) = 'Negative Binomial(1.9534)',
## NB(1.9901) = 'Negative Binomial(1.9901)'
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry lst_wet
## m3 3.028 0.6968 5.055
## m6 3.605 0.5855 0.02865
## m5 3.642 0.5870 0.09016
## m4 4.098 0.6651 -0.01575
## m1 3.092 0.01113 0.6556
## m2 5.150 0.3401 -0.2013
## df logLik AICc delta weight
## m3 3 -233.754 474.4 0.00 0.325
## m6 3 -234.080 475.1 0.65 0.235
## m5 3 -234.270 475.5 1.03 0.194
## m4 3 -234.820 476.6 2.13 0.112
## m1 3 -234.975 476.9 2.44 0.096
## m2 3 -235.887 478.7 4.27 0.039
## Models ranked by AICc(x)
## Error in nrow(concord.out): object 'concord.out' not found
sites<-c("puertorico", "macae", "frenchguiana", "costarica", "colombia", "argentina")
concord.out8<-concord.magic(sites, "totalbio", noargco123data, 10, 2, "nb")#a mess!
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 2.4720 0.4239
## m4 1.6180 0.6087 0.2398 -0.77580
## m2 2.1990 0.4694 0.2328
## m1 2.1430 0.4821 0.05151
## m9 1.9040 0.5218 0.07683 0.2006
## m3 2.0190 0.5017 0.06564 0.01157
## m10 1.3880 0.6509 0.06969 0.2070 -0.78870
## m11 2.4550 0.4321 0.05266 0.0546 -0.03238
## m12 -0.9053 1.0050 0.39860 0.0535 0.24970 0.02689
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m4
## m2
## m1
## m9 -0.20740
## m3
## m10 -0.20880 0.06291
## m11 -0.04674
## m12 -0.08964 -0.95260
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 family
## m0 NB(3.7712)
## m4 NB(4.5153)
## m2 NB(3.9858)
## m1 NB(3.8084)
## m9 NB(4.2351)
## m3 NB(3.8101)
## m10 NB(4.9096)
## m11 0.1852 NB(4.3925)
## m12 0.2001 -0.9477 NB(6.6877)
## init.theta df logLik AICc delta weight
## m0 3.77 3 -168.763 344.4 0.00 0.326
## m4 4.52 5 -166.006 344.5 0.06 0.316
## m2 3.99 4 -167.910 345.4 0.97 0.201
## m1 3.81 4 -168.611 346.8 2.37 0.100
## m9 4.24 6 -166.973 349.6 5.15 0.025
## m3 3.81 5 -168.603 349.7 5.26 0.024
## m10 4.91 8 -164.738 352.3 7.88 0.006
## m11 4.39 8 -166.371 355.6 11.15 0.001
## m12 6.69 11 -159.949 356.6 12.11 0.001
## Abbreviations:
## family: NB(3.7712) = 'Negative Binomial(3.7712)',
## NB(3.8084) = 'Negative Binomial(3.8084)',
## NB(3.8101) = 'Negative Binomial(3.8101)',
## NB(3.9858) = 'Negative Binomial(3.9858)',
## NB(4.2351) = 'Negative Binomial(4.2351)',
## NB(4.3925) = 'Negative Binomial(4.3925)',
## NB(4.5153) = 'Negative Binomial(4.5153)',
## NB(4.9096) = 'Negative Binomial(4.9096)',
## NB(6.6877) = 'Negative Binomial(6.6877)'
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 2.4720 0.4239
## m4 1.6170 0.6089 0.23980 -0.77620
## m2 2.1990 0.4694 0.23280
## m1 2.1430 0.4821 0.05151
## m9 1.9050 0.5215 0.07689 0.20070
## m3 2.0190 0.5017 0.06565 0.01158
## m10 1.3900 0.6505 0.06955 0.20690 -0.78880
## m11 2.4630 0.4308 0.05248 0.05381 -0.03272
## m12 -0.8911 1.0020 0.39780 0.04841 0.24930 0.03333
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m4
## m2
## m1
## m9 -0.20770
## m3
## m10 -0.20920 0.06418
## m11 -0.04655
## m12 -0.08948 -0.95030
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik
## m0 2 -168.763
## m4 4 -166.233
## m2 3 -167.932
## m1 3 -168.612
## m9 5 -167.070
## m3 4 -168.604
## m10 7 -165.208
## m11 0.1865 7 -166.536
## m12 0.2047 -0.9535 10 -161.944
## AICc delta weight
## m0 342.0 0.00 0.315
## m4 342.1 0.10 0.301
## m2 342.8 0.82 0.210
## m1 344.1 2.18 0.106
## m9 346.6 4.67 0.031
## m3 346.8 4.84 0.028
## m10 349.5 7.54 0.007
## m11 352.2 10.19 0.002
## m12 355.5 13.50 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 2.4720 0.4239
## m4 1.6180 0.6087 0.2398 -0.77580
## m2 2.1990 0.4694 0.2328
## m1 2.1430 0.4821 0.05151
## m9 1.9040 0.5218 0.07683 0.2006
## m3 2.0190 0.5017 0.06564 0.01157
## m10 1.3880 0.6509 0.06969 0.2070 -0.78870
## m11 2.4550 0.4321 0.05266 0.0546 -0.03238
## m12 -0.9053 1.0050 0.39860 0.0535 0.24970 0.02689
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m4
## m2
## m1
## m9 -0.20740
## m3
## m10 -0.20880 0.06291
## m11 -0.04674
## m12 -0.08964 -0.95260
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 family
## m0 NB(3.7712)
## m4 NB(4.5153)
## m2 NB(3.9858)
## m1 NB(3.8084)
## m9 NB(4.2351)
## m3 NB(3.8101)
## m10 NB(4.9096)
## m11 0.1852 NB(4.3925)
## m12 0.2001 -0.9477 NB(6.6877)
## init.theta df logLik AICc delta weight
## m0 3.77 3 -168.763 344.4 0.00 0.326
## m4 4.52 5 -166.006 344.5 0.06 0.316
## m2 3.99 4 -167.910 345.4 0.97 0.201
## m1 3.81 4 -168.611 346.8 2.37 0.100
## m9 4.24 6 -166.973 349.6 5.15 0.025
## m3 3.81 5 -168.603 349.7 5.26 0.024
## m10 4.91 8 -164.738 352.3 7.88 0.006
## m11 4.39 8 -166.371 355.6 11.15 0.001
## m12 6.69 11 -159.949 356.6 12.11 0.001
## Abbreviations:
## family: NB(3.7712) = 'Negative Binomial(3.7712)',
## NB(3.8084) = 'Negative Binomial(3.8084)',
## NB(3.8101) = 'Negative Binomial(3.8101)',
## NB(3.9858) = 'Negative Binomial(3.9858)',
## NB(4.2351) = 'Negative Binomial(4.2351)',
## NB(4.3925) = 'Negative Binomial(4.3925)',
## NB(4.5153) = 'Negative Binomial(4.5153)',
## NB(4.9096) = 'Negative Binomial(4.9096)',
## NB(6.6877) = 'Negative Binomial(6.6877)'
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 2.4720 0.4239
## m4 1.6170 0.6089 0.23980 -0.77620
## m2 2.1990 0.4694 0.23280
## m1 2.1430 0.4821 0.05151
## m9 1.9050 0.5215 0.07689 0.20070
## m3 2.0190 0.5017 0.06565 0.01158
## m10 1.3900 0.6505 0.06955 0.20690 -0.78880
## m11 2.4630 0.4308 0.05248 0.05381 -0.03272
## m12 -0.8911 1.0020 0.39780 0.04841 0.24930 0.03333
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m4
## m2
## m1
## m9 -0.20770
## m3
## m10 -0.20920 0.06418
## m11 -0.04655
## m12 -0.08948 -0.95030
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik
## m0 2 -168.763
## m4 4 -166.233
## m2 3 -167.932
## m1 3 -168.612
## m9 5 -167.070
## m3 4 -168.604
## m10 7 -165.208
## m11 0.1865 7 -166.536
## m12 0.2047 -0.9535 10 -161.944
## AICc delta weight
## m0 342.0 0.00 0.315
## m4 342.1 0.10 0.301
## m2 342.8 0.82 0.210
## m1 344.1 2.18 0.106
## m9 346.6 4.67 0.031
## m3 346.8 4.84 0.028
## m10 349.5 7.54 0.007
## m11 352.2 10.19 0.002
## m12 355.5 13.50 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 2.4720 0.4239
## m4 1.6180 0.6087 0.2398 -0.77580
## m2 2.1990 0.4694 0.2328
## m1 2.1430 0.4821 0.05151
## m9 1.9040 0.5218 0.07683 0.2006
## m3 2.0190 0.5017 0.06564 0.01157
## m10 1.3880 0.6509 0.06969 0.2070 -0.78870
## m11 2.4550 0.4321 0.05266 0.0546 -0.03238
## m12 -0.9053 1.0050 0.39860 0.0535 0.24970 0.02689
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m4
## m2
## m1
## m9 -0.20740
## m3
## m10 -0.20880 0.06291
## m11 -0.04674
## m12 -0.08964 -0.95260
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 family
## m0 NB(3.7712)
## m4 NB(4.5153)
## m2 NB(3.9858)
## m1 NB(3.8084)
## m9 NB(4.2351)
## m3 NB(3.8101)
## m10 NB(4.9096)
## m11 0.1852 NB(4.3925)
## m12 0.2001 -0.9477 NB(6.6877)
## init.theta df logLik AICc delta weight
## m0 3.77 3 -168.763 344.4 0.00 0.326
## m4 4.52 5 -166.006 344.5 0.06 0.316
## m2 3.99 4 -167.910 345.4 0.97 0.201
## m1 3.81 4 -168.611 346.8 2.37 0.100
## m9 4.24 6 -166.973 349.6 5.15 0.025
## m3 3.81 5 -168.603 349.7 5.26 0.024
## m10 4.91 8 -164.738 352.3 7.88 0.006
## m11 4.39 8 -166.371 355.6 11.15 0.001
## m12 6.69 11 -159.949 356.6 12.11 0.001
## Abbreviations:
## family: NB(3.7712) = 'Negative Binomial(3.7712)',
## NB(3.8084) = 'Negative Binomial(3.8084)',
## NB(3.8101) = 'Negative Binomial(3.8101)',
## NB(3.9858) = 'Negative Binomial(3.9858)',
## NB(4.2351) = 'Negative Binomial(4.2351)',
## NB(4.3925) = 'Negative Binomial(4.3925)',
## NB(4.5153) = 'Negative Binomial(4.5153)',
## NB(4.9096) = 'Negative Binomial(4.9096)',
## NB(6.6877) = 'Negative Binomial(6.6877)'
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 2.4720 0.4239
## m4 1.6170 0.6089 0.23980 -0.77620
## m2 2.1990 0.4694 0.23280
## m1 2.1430 0.4821 0.05151
## m9 1.9050 0.5215 0.07689 0.20070
## m3 2.0190 0.5017 0.06565 0.01158
## m10 1.3900 0.6505 0.06955 0.20690 -0.78880
## m11 2.4630 0.4308 0.05248 0.05381 -0.03272
## m12 -0.8911 1.0020 0.39780 0.04841 0.24930 0.03333
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m4
## m2
## m1
## m9 -0.20770
## m3
## m10 -0.20920 0.06418
## m11 -0.04655
## m12 -0.08948 -0.95030
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik
## m0 2 -168.763
## m4 4 -166.233
## m2 3 -167.932
## m1 3 -168.612
## m9 5 -167.070
## m3 4 -168.604
## m10 7 -165.208
## m11 0.1865 7 -166.536
## m12 0.2047 -0.9535 10 -161.944
## AICc delta weight
## m0 342.0 0.00 0.315
## m4 342.1 0.10 0.301
## m2 342.8 0.82 0.210
## m1 344.1 2.18 0.106
## m9 346.6 4.67 0.031
## m3 346.8 4.84 0.028
## m10 349.5 7.54 0.007
## m11 352.2 10.19 0.002
## m12 355.5 13.50 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 2.4720 0.4239
## m4 1.6180 0.6087 0.2398 -0.77580
## m2 2.1990 0.4694 0.2328
## m1 2.1430 0.4821 0.05151
## m9 1.9040 0.5218 0.07683 0.2006
## m3 2.0190 0.5017 0.06564 0.01157
## m10 1.3880 0.6509 0.06969 0.2070 -0.78870
## m11 2.4550 0.4321 0.05266 0.0546 -0.03238
## m12 -0.9053 1.0050 0.39860 0.0535 0.24970 0.02689
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m4
## m2
## m1
## m9 -0.20740
## m3
## m10 -0.20880 0.06291
## m11 -0.04674
## m12 -0.08964 -0.95260
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 family
## m0 NB(3.7712)
## m4 NB(4.5153)
## m2 NB(3.9858)
## m1 NB(3.8084)
## m9 NB(4.2351)
## m3 NB(3.8101)
## m10 NB(4.9096)
## m11 0.1852 NB(4.3925)
## m12 0.2001 -0.9477 NB(6.6877)
## init.theta df logLik AICc delta weight
## m0 3.77 3 -168.763 344.4 0.00 0.326
## m4 4.52 5 -166.006 344.5 0.06 0.316
## m2 3.99 4 -167.910 345.4 0.97 0.201
## m1 3.81 4 -168.611 346.8 2.37 0.100
## m9 4.24 6 -166.973 349.6 5.15 0.025
## m3 3.81 5 -168.603 349.7 5.26 0.024
## m10 4.91 8 -164.738 352.3 7.88 0.006
## m11 4.39 8 -166.371 355.6 11.15 0.001
## m12 6.69 11 -159.949 356.6 12.11 0.001
## Abbreviations:
## family: NB(3.7712) = 'Negative Binomial(3.7712)',
## NB(3.8084) = 'Negative Binomial(3.8084)',
## NB(3.8101) = 'Negative Binomial(3.8101)',
## NB(3.9858) = 'Negative Binomial(3.9858)',
## NB(4.2351) = 'Negative Binomial(4.2351)',
## NB(4.3925) = 'Negative Binomial(4.3925)',
## NB(4.5153) = 'Negative Binomial(4.5153)',
## NB(4.9096) = 'Negative Binomial(4.9096)',
## NB(6.6877) = 'Negative Binomial(6.6877)'
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 2.4720 0.4239
## m4 1.6170 0.6089 0.23980 -0.77620
## m2 2.1990 0.4694 0.23280
## m1 2.1430 0.4821 0.05151
## m9 1.9050 0.5215 0.07689 0.20070
## m3 2.0190 0.5017 0.06565 0.01158
## m10 1.3900 0.6505 0.06955 0.20690 -0.78880
## m11 2.4630 0.4308 0.05248 0.05381 -0.03272
## m12 -0.8911 1.0020 0.39780 0.04841 0.24930 0.03333
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m4
## m2
## m1
## m9 -0.20770
## m3
## m10 -0.20920 0.06418
## m11 -0.04655
## m12 -0.08948 -0.95030
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik
## m0 2 -168.763
## m4 4 -166.233
## m2 3 -167.932
## m1 3 -168.612
## m9 5 -167.070
## m3 4 -168.604
## m10 7 -165.208
## m11 0.1865 7 -166.536
## m12 0.2047 -0.9535 10 -161.944
## AICc delta weight
## m0 342.0 0.00 0.315
## m4 342.1 0.10 0.301
## m2 342.8 0.82 0.210
## m1 344.1 2.18 0.106
## m9 346.6 4.67 0.031
## m3 346.8 4.84 0.028
## m10 349.5 7.54 0.007
## m11 352.2 10.19 0.002
## m12 355.5 13.50 0.000
## Models ranked by AICc(x)
## Warning in matrix(value, n, p): data length [11] is not a sub-multiple or
## multiple of the number of columns [10]
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 2.4720 0.4239
## m4 1.6180 0.6087 0.2398 -0.77580
## m2 2.1990 0.4694 0.2328
## m1 2.1430 0.4821 0.05151
## m9 1.9040 0.5218 0.07683 0.2006
## m3 2.0190 0.5017 0.06564 0.01157
## m10 1.3880 0.6509 0.06969 0.2070 -0.78870
## m11 2.4550 0.4321 0.05266 0.0546 -0.03238
## m12 -0.9053 1.0050 0.39860 0.0535 0.24970 0.02689
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m4
## m2
## m1
## m9 -0.20740
## m3
## m10 -0.20880 0.06291
## m11 -0.04674
## m12 -0.08964 -0.95260
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 family
## m0 NB(3.7712)
## m4 NB(4.5153)
## m2 NB(3.9858)
## m1 NB(3.8084)
## m9 NB(4.2351)
## m3 NB(3.8101)
## m10 NB(4.9096)
## m11 0.1852 NB(4.3925)
## m12 0.2001 -0.9477 NB(6.6877)
## init.theta df logLik AICc delta weight
## m0 3.77 3 -168.763 344.4 0.00 0.326
## m4 4.52 5 -166.006 344.5 0.06 0.316
## m2 3.99 4 -167.910 345.4 0.97 0.201
## m1 3.81 4 -168.611 346.8 2.37 0.100
## m9 4.24 6 -166.973 349.6 5.15 0.025
## m3 3.81 5 -168.603 349.7 5.26 0.024
## m10 4.91 8 -164.738 352.3 7.88 0.006
## m11 4.39 8 -166.371 355.6 11.15 0.001
## m12 6.69 11 -159.949 356.6 12.11 0.001
## Abbreviations:
## family: NB(3.7712) = 'Negative Binomial(3.7712)',
## NB(3.8084) = 'Negative Binomial(3.8084)',
## NB(3.8101) = 'Negative Binomial(3.8101)',
## NB(3.9858) = 'Negative Binomial(3.9858)',
## NB(4.2351) = 'Negative Binomial(4.2351)',
## NB(4.3925) = 'Negative Binomial(4.3925)',
## NB(4.5153) = 'Negative Binomial(4.5153)',
## NB(4.9096) = 'Negative Binomial(4.9096)',
## NB(6.6877) = 'Negative Binomial(6.6877)'
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 2.4720 0.4239
## m4 1.6170 0.6089 0.23980 -0.77620
## m2 2.1990 0.4694 0.23280
## m1 2.1430 0.4821 0.05151
## m9 1.9050 0.5215 0.07689 0.20070
## m3 2.0190 0.5017 0.06565 0.01158
## m10 1.3900 0.6505 0.06955 0.20690 -0.78880
## m11 2.4630 0.4308 0.05248 0.05381 -0.03272
## m12 -0.8911 1.0020 0.39780 0.04841 0.24930 0.03333
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m4
## m2
## m1
## m9 -0.20770
## m3
## m10 -0.20920 0.06418
## m11 -0.04655
## m12 -0.08948 -0.95030
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik
## m0 2 -168.763
## m4 4 -166.233
## m2 3 -167.932
## m1 3 -168.612
## m9 5 -167.070
## m3 4 -168.604
## m10 7 -165.208
## m11 0.1865 7 -166.536
## m12 0.2047 -0.9535 10 -161.944
## AICc delta weight
## m0 342.0 0.00 0.315
## m4 342.1 0.10 0.301
## m2 342.8 0.82 0.210
## m1 344.1 2.18 0.106
## m9 346.6 4.67 0.031
## m3 346.8 4.84 0.028
## m10 349.5 7.54 0.007
## m11 352.2 10.19 0.002
## m12 355.5 13.50 0.000
## Models ranked by AICc(x)
## Warning in matrix(value, n, p): data length [11] is not a sub-multiple or
## multiple of the number of columns [10]
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m2 6.631 -0.2007 -0.3550
## m9 6.686 -0.2144 -0.10830 -0.2877
## m4 8.236 -0.4180 -0.3431 -0.33800
## m1 9.572 -0.6303 -0.12860
## m0 10.890 -0.8151
## m3 9.576 -0.6356 -0.09304 0.034130
## m10 8.662 -0.4849 -0.12210 -0.2700 -0.34570
## m11 7.282 -0.3007 -0.10010 -0.1849 -0.001132
## m12 7.620 -0.3472 -0.04603 -0.1751 0.086830 -0.08628
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m2
## m9 0.18190
## m4
## m1
## m0
## m3
## m10 0.19240 0.06226
## m11 0.06942
## m12 0.07230 -0.14900
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 family
## m2 NB(5.5302)
## m9 NB(6.4273)
## m4 NB(5.6931)
## m1 NB(5.1321)
## m0 NB(4.7023)
## m3 NB(5.1635)
## m10 NB(6.6864)
## m11 -0.1105 NB(6.601)
## m12 -0.1168 -0.2529 NB(7.0323)
## init.theta df logLik AICc delta weight
## m2 5.53 4 -172.748 355.1 0.00 0.386
## m9 6.43 6 -170.438 356.5 1.43 0.188
## m4 5.69 5 -172.298 357.1 2.00 0.142
## m1 5.13 4 -173.917 357.4 2.34 0.120
## m0 4.7 3 -175.288 357.5 2.40 0.116
## m3 5.16 5 -173.820 360.1 5.04 0.031
## m10 6.69 8 -169.831 362.5 7.42 0.009
## m11 6.6 8 -170.027 362.9 7.82 0.008
## m12 7.03 11 -169.049 374.8 19.67 0.000
## Abbreviations:
## family: NB(4.7023) = 'Negative Binomial(4.7023)',
## NB(5.1321) = 'Negative Binomial(5.1321)',
## NB(5.1635) = 'Negative Binomial(5.1635)',
## NB(5.5302) = 'Negative Binomial(5.5302)',
## NB(5.6931) = 'Negative Binomial(5.6931)',
## NB(6.4273) = 'Negative Binomial(6.4273)',
## NB(6.601) = 'Negative Binomial(6.601)',
## NB(6.6864) = 'Negative Binomial(6.6864)',
## NB(7.0323) = 'Negative Binomial(7.0323)'
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m2 6.631 -0.2007 -0.3551
## m9 6.689 -0.2148 -0.10830 -0.2877
## m4 8.236 -0.4179 -0.3431 -0.33790
## m1 9.570 -0.6301 -0.12860
## m0 10.890 -0.8147
## m3 9.574 -0.6352 -0.09307 0.034100
## m10 8.665 -0.4855 -0.12200 -0.2699 -0.34570
## m11 7.284 -0.3009 -0.10010 -0.1850 -0.001157
## m12 7.613 -0.3463 -0.04562 -0.1752 0.087390 -0.08469
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m2
## m9 0.18190
## m4
## m1
## m0
## m3
## m10 0.19250 0.06228
## m11 0.06960
## m12 0.07262 -0.14990
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik
## m2 3 -172.748
## m9 5 -170.599
## m4 4 -172.304
## m1 3 -173.961
## m0 2 -175.499
## m3 4 -173.856
## m10 7 -170.084
## m11 -0.1105 7 -170.249
## m12 -0.1167 -0.2543 10 -169.447
## AICc delta weight
## m2 352.4 0.00 0.384
## m9 353.7 1.28 0.203
## m4 354.2 1.79 0.157
## m1 354.8 2.43 0.114
## m0 355.4 3.02 0.085
## m3 357.3 4.89 0.033
## m10 359.3 6.84 0.013
## m11 359.6 7.17 0.011
## m12 370.5 18.05 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m2 6.631 -0.2007 -0.3550
## m9 6.686 -0.2144 -0.10830 -0.2877
## m4 8.236 -0.4180 -0.3431 -0.33800
## m1 9.572 -0.6303 -0.12860
## m0 10.890 -0.8151
## m3 9.576 -0.6356 -0.09304 0.034130
## m10 8.662 -0.4849 -0.12210 -0.2700 -0.34570
## m11 7.282 -0.3007 -0.10010 -0.1849 -0.001132
## m12 7.620 -0.3472 -0.04603 -0.1751 0.086830 -0.08628
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m2
## m9 0.18190
## m4
## m1
## m0
## m3
## m10 0.19240 0.06226
## m11 0.06942
## m12 0.07230 -0.14900
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 family
## m2 NB(5.5302)
## m9 NB(6.4273)
## m4 NB(5.6931)
## m1 NB(5.1321)
## m0 NB(4.7023)
## m3 NB(5.1635)
## m10 NB(6.6864)
## m11 -0.1105 NB(6.601)
## m12 -0.1168 -0.2529 NB(7.0323)
## init.theta df logLik AICc delta weight
## m2 5.53 4 -172.748 355.1 0.00 0.386
## m9 6.43 6 -170.438 356.5 1.43 0.188
## m4 5.69 5 -172.298 357.1 2.00 0.142
## m1 5.13 4 -173.917 357.4 2.34 0.120
## m0 4.7 3 -175.288 357.5 2.40 0.116
## m3 5.16 5 -173.820 360.1 5.04 0.031
## m10 6.69 8 -169.831 362.5 7.42 0.009
## m11 6.6 8 -170.027 362.9 7.82 0.008
## m12 7.03 11 -169.049 374.8 19.67 0.000
## Abbreviations:
## family: NB(4.7023) = 'Negative Binomial(4.7023)',
## NB(5.1321) = 'Negative Binomial(5.1321)',
## NB(5.1635) = 'Negative Binomial(5.1635)',
## NB(5.5302) = 'Negative Binomial(5.5302)',
## NB(5.6931) = 'Negative Binomial(5.6931)',
## NB(6.4273) = 'Negative Binomial(6.4273)',
## NB(6.601) = 'Negative Binomial(6.601)',
## NB(6.6864) = 'Negative Binomial(6.6864)',
## NB(7.0323) = 'Negative Binomial(7.0323)'
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m2 6.631 -0.2007 -0.3551
## m9 6.689 -0.2148 -0.10830 -0.2877
## m4 8.236 -0.4179 -0.3431 -0.33790
## m1 9.570 -0.6301 -0.12860
## m0 10.890 -0.8147
## m3 9.574 -0.6352 -0.09307 0.034100
## m10 8.665 -0.4855 -0.12200 -0.2699 -0.34570
## m11 7.284 -0.3009 -0.10010 -0.1850 -0.001157
## m12 7.613 -0.3463 -0.04562 -0.1752 0.087390 -0.08469
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m2
## m9 0.18190
## m4
## m1
## m0
## m3
## m10 0.19250 0.06228
## m11 0.06960
## m12 0.07262 -0.14990
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik
## m2 3 -172.748
## m9 5 -170.599
## m4 4 -172.304
## m1 3 -173.961
## m0 2 -175.499
## m3 4 -173.856
## m10 7 -170.084
## m11 -0.1105 7 -170.249
## m12 -0.1167 -0.2543 10 -169.447
## AICc delta weight
## m2 352.4 0.00 0.384
## m9 353.7 1.28 0.203
## m4 354.2 1.79 0.157
## m1 354.8 2.43 0.114
## m0 355.4 3.02 0.085
## m3 357.3 4.89 0.033
## m10 359.3 6.84 0.013
## m11 359.6 7.17 0.011
## m12 370.5 18.05 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m2 6.631 -0.2007 -0.3550
## m9 6.686 -0.2144 -0.10830 -0.2877
## m4 8.236 -0.4180 -0.3431 -0.33800
## m1 9.572 -0.6303 -0.12860
## m0 10.890 -0.8151
## m3 9.576 -0.6356 -0.09304 0.034130
## m10 8.662 -0.4849 -0.12210 -0.2700 -0.34570
## m11 7.282 -0.3007 -0.10010 -0.1849 -0.001132
## m12 7.620 -0.3472 -0.04603 -0.1751 0.086830 -0.08628
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m2
## m9 0.18190
## m4
## m1
## m0
## m3
## m10 0.19240 0.06226
## m11 0.06942
## m12 0.07230 -0.14900
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 family
## m2 NB(5.5302)
## m9 NB(6.4273)
## m4 NB(5.6931)
## m1 NB(5.1321)
## m0 NB(4.7023)
## m3 NB(5.1635)
## m10 NB(6.6864)
## m11 -0.1105 NB(6.601)
## m12 -0.1168 -0.2529 NB(7.0323)
## init.theta df logLik AICc delta weight
## m2 5.53 4 -172.748 355.1 0.00 0.386
## m9 6.43 6 -170.438 356.5 1.43 0.188
## m4 5.69 5 -172.298 357.1 2.00 0.142
## m1 5.13 4 -173.917 357.4 2.34 0.120
## m0 4.7 3 -175.288 357.5 2.40 0.116
## m3 5.16 5 -173.820 360.1 5.04 0.031
## m10 6.69 8 -169.831 362.5 7.42 0.009
## m11 6.6 8 -170.027 362.9 7.82 0.008
## m12 7.03 11 -169.049 374.8 19.67 0.000
## Abbreviations:
## family: NB(4.7023) = 'Negative Binomial(4.7023)',
## NB(5.1321) = 'Negative Binomial(5.1321)',
## NB(5.1635) = 'Negative Binomial(5.1635)',
## NB(5.5302) = 'Negative Binomial(5.5302)',
## NB(5.6931) = 'Negative Binomial(5.6931)',
## NB(6.4273) = 'Negative Binomial(6.4273)',
## NB(6.601) = 'Negative Binomial(6.601)',
## NB(6.6864) = 'Negative Binomial(6.6864)',
## NB(7.0323) = 'Negative Binomial(7.0323)'
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m2 6.631 -0.2007 -0.3551
## m9 6.689 -0.2148 -0.10830 -0.2877
## m4 8.236 -0.4179 -0.3431 -0.33790
## m1 9.570 -0.6301 -0.12860
## m0 10.890 -0.8147
## m3 9.574 -0.6352 -0.09307 0.034100
## m10 8.665 -0.4855 -0.12200 -0.2699 -0.34570
## m11 7.284 -0.3009 -0.10010 -0.1850 -0.001157
## m12 7.613 -0.3463 -0.04562 -0.1752 0.087390 -0.08469
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m2
## m9 0.18190
## m4
## m1
## m0
## m3
## m10 0.19250 0.06228
## m11 0.06960
## m12 0.07262 -0.14990
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik
## m2 3 -172.748
## m9 5 -170.599
## m4 4 -172.304
## m1 3 -173.961
## m0 2 -175.499
## m3 4 -173.856
## m10 7 -170.084
## m11 -0.1105 7 -170.249
## m12 -0.1167 -0.2543 10 -169.447
## AICc delta weight
## m2 352.4 0.00 0.384
## m9 353.7 1.28 0.203
## m4 354.2 1.79 0.157
## m1 354.8 2.43 0.114
## m0 355.4 3.02 0.085
## m3 357.3 4.89 0.033
## m10 359.3 6.84 0.013
## m11 359.6 7.17 0.011
## m12 370.5 18.05 0.000
## Models ranked by AICc(x)
## Warning in matrix(value, n, p): data length [11] is not a sub-multiple or
## multiple of the number of columns [10]
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m2 6.631 -0.2007 -0.3550
## m9 6.686 -0.2144 -0.10830 -0.2877
## m4 8.236 -0.4180 -0.3431 -0.33800
## m1 9.572 -0.6303 -0.12860
## m0 10.890 -0.8151
## m3 9.576 -0.6356 -0.09304 0.034130
## m10 8.662 -0.4849 -0.12210 -0.2700 -0.34570
## m11 7.282 -0.3007 -0.10010 -0.1849 -0.001132
## m12 7.620 -0.3472 -0.04603 -0.1751 0.086830 -0.08628
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m2
## m9 0.18190
## m4
## m1
## m0
## m3
## m10 0.19240 0.06226
## m11 0.06942
## m12 0.07230 -0.14900
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 family
## m2 NB(5.5302)
## m9 NB(6.4273)
## m4 NB(5.6931)
## m1 NB(5.1321)
## m0 NB(4.7023)
## m3 NB(5.1635)
## m10 NB(6.6864)
## m11 -0.1105 NB(6.601)
## m12 -0.1168 -0.2529 NB(7.0323)
## init.theta df logLik AICc delta weight
## m2 5.53 4 -172.748 355.1 0.00 0.386
## m9 6.43 6 -170.438 356.5 1.43 0.188
## m4 5.69 5 -172.298 357.1 2.00 0.142
## m1 5.13 4 -173.917 357.4 2.34 0.120
## m0 4.7 3 -175.288 357.5 2.40 0.116
## m3 5.16 5 -173.820 360.1 5.04 0.031
## m10 6.69 8 -169.831 362.5 7.42 0.009
## m11 6.6 8 -170.027 362.9 7.82 0.008
## m12 7.03 11 -169.049 374.8 19.67 0.000
## Abbreviations:
## family: NB(4.7023) = 'Negative Binomial(4.7023)',
## NB(5.1321) = 'Negative Binomial(5.1321)',
## NB(5.1635) = 'Negative Binomial(5.1635)',
## NB(5.5302) = 'Negative Binomial(5.5302)',
## NB(5.6931) = 'Negative Binomial(5.6931)',
## NB(6.4273) = 'Negative Binomial(6.4273)',
## NB(6.601) = 'Negative Binomial(6.601)',
## NB(6.6864) = 'Negative Binomial(6.6864)',
## NB(7.0323) = 'Negative Binomial(7.0323)'
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m2 6.631 -0.2007 -0.3551
## m9 6.689 -0.2148 -0.10830 -0.2877
## m4 8.236 -0.4179 -0.3431 -0.33790
## m1 9.570 -0.6301 -0.12860
## m0 10.890 -0.8147
## m3 9.574 -0.6352 -0.09307 0.034100
## m10 8.665 -0.4855 -0.12200 -0.2699 -0.34570
## m11 7.284 -0.3009 -0.10010 -0.1850 -0.001157
## m12 7.613 -0.3463 -0.04562 -0.1752 0.087390 -0.08469
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m2
## m9 0.18190
## m4
## m1
## m0
## m3
## m10 0.19250 0.06228
## m11 0.06960
## m12 0.07262 -0.14990
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik
## m2 3 -172.748
## m9 5 -170.599
## m4 4 -172.304
## m1 3 -173.961
## m0 2 -175.499
## m3 4 -173.856
## m10 7 -170.084
## m11 -0.1105 7 -170.249
## m12 -0.1167 -0.2543 10 -169.447
## AICc delta weight
## m2 352.4 0.00 0.384
## m9 353.7 1.28 0.203
## m4 354.2 1.79 0.157
## m1 354.8 2.43 0.114
## m0 355.4 3.02 0.085
## m3 357.3 4.89 0.033
## m10 359.3 6.84 0.013
## m11 359.6 7.17 0.011
## m12 370.5 18.05 0.000
## Models ranked by AICc(x)
## Warning in matrix(value, n, p): data length [11] is not a sub-multiple or
## multiple of the number of columns [10]
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 2.511 0.4500
## m2 1.607 0.6159 0.2228
## m4 2.533 0.4779 0.1986 -0.5864
## m1 2.864 0.3884 0.10010
## m3 2.823 0.3909 0.13440 0.03027
## m9 2.117 0.5245 0.09022 0.2246
## m10 3.122 0.3745 0.10010 0.1992 -0.6249
## m11 2.092 0.5244 0.12230 0.2488 0.02862
## m12 3.123 0.3665 0.15880 0.2261 0.04745 -0.5665
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m2
## m4
## m1
## m3
## m9 0.12710
## m10 0.13240 -0.0107
## m11 0.09981
## m12 0.10010 -0.1004
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 family
## m0 NB(4.9638)
## m2 NB(5.2761)
## m4 NB(5.7771)
## m1 NB(5.237)
## m3 NB(5.2649)
## m9 NB(5.6641)
## m10 NB(6.3327)
## m11 -0.02578 NB(5.703)
## m12 -0.03037 -0.06857 NB(6.3887)
## init.theta df logLik AICc delta weight
## m0 4.96 3 -165.387 337.7 0.00 0.322
## m2 5.28 4 -164.462 338.5 0.83 0.213
## m4 5.78 5 -163.109 338.7 1.02 0.193
## m1 5.24 4 -164.578 338.8 1.06 0.189
## m3 5.26 5 -164.497 341.5 3.80 0.048
## m9 5.66 6 -163.403 342.5 4.76 0.030
## m10 6.33 8 -161.754 346.4 8.67 0.004
## m11 5.7 8 -163.300 349.5 11.76 0.001
## m12 6.39 11 -161.623 359.9 22.22 0.000
## Abbreviations:
## family: NB(4.9638) = 'Negative Binomial(4.9638)',
## NB(5.237) = 'Negative Binomial(5.237)',
## NB(5.2649) = 'Negative Binomial(5.2649)',
## NB(5.2761) = 'Negative Binomial(5.2761)',
## NB(5.6641) = 'Negative Binomial(5.6641)',
## NB(5.703) = 'Negative Binomial(5.703)',
## NB(5.7771) = 'Negative Binomial(5.7771)',
## NB(6.3327) = 'Negative Binomial(6.3327)',
## NB(6.3887) = 'Negative Binomial(6.3887)'
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 2.511 0.4500
## m2 1.607 0.6159 0.2228
## m1 2.864 0.3884 0.10010
## m4 2.533 0.4780 0.1986 -0.5863
## m3 2.823 0.3909 0.13440 0.03028
## m9 2.118 0.5243 0.09018 0.2247
## m10 3.121 0.3745 0.10010 0.1993 -0.6247
## m11 2.093 0.5241 0.12230 0.2488 0.02861
## m12 3.123 0.3664 0.15910 0.2261 0.04766 -0.5659
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m2
## m1
## m4
## m3
## m9 0.12720
## m10 0.13240 -0.01064
## m11 0.09984
## m12 0.10010 -0.10130
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik
## m0 2 -165.387
## m2 3 -164.489
## m1 3 -164.599
## m4 4 -163.269
## m3 4 -164.522
## m9 5 -163.525
## m10 7 -162.149
## m11 -0.02581 7 -163.435
## m12 -0.03037 -0.06918 10 -162.045
## AICc delta weight
## m0 335.2 0.00 0.304
## m2 335.9 0.68 0.216
## m1 336.1 0.90 0.193
## m4 336.1 0.92 0.192
## m3 338.6 3.43 0.055
## m9 339.6 4.33 0.035
## m10 343.4 8.17 0.005
## m11 346.0 10.74 0.001
## m12 355.7 20.45 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 2.511 0.4500
## m2 1.607 0.6159 0.2228
## m4 2.533 0.4779 0.1986 -0.5864
## m1 2.864 0.3884 0.10010
## m3 2.823 0.3909 0.13440 0.03027
## m9 2.117 0.5245 0.09022 0.2246
## m10 3.122 0.3745 0.10010 0.1992 -0.6249
## m11 2.092 0.5244 0.12230 0.2488 0.02862
## m12 3.123 0.3665 0.15880 0.2261 0.04745 -0.5665
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m2
## m4
## m1
## m3
## m9 0.12710
## m10 0.13240 -0.0107
## m11 0.09981
## m12 0.10010 -0.1004
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 family
## m0 NB(4.9638)
## m2 NB(5.2761)
## m4 NB(5.7771)
## m1 NB(5.237)
## m3 NB(5.2649)
## m9 NB(5.6641)
## m10 NB(6.3327)
## m11 -0.02578 NB(5.703)
## m12 -0.03037 -0.06857 NB(6.3887)
## init.theta df logLik AICc delta weight
## m0 4.96 3 -165.387 337.7 0.00 0.322
## m2 5.28 4 -164.462 338.5 0.83 0.213
## m4 5.78 5 -163.109 338.7 1.02 0.193
## m1 5.24 4 -164.578 338.8 1.06 0.189
## m3 5.26 5 -164.497 341.5 3.80 0.048
## m9 5.66 6 -163.403 342.5 4.76 0.030
## m10 6.33 8 -161.754 346.4 8.67 0.004
## m11 5.7 8 -163.300 349.5 11.76 0.001
## m12 6.39 11 -161.623 359.9 22.22 0.000
## Abbreviations:
## family: NB(4.9638) = 'Negative Binomial(4.9638)',
## NB(5.237) = 'Negative Binomial(5.237)',
## NB(5.2649) = 'Negative Binomial(5.2649)',
## NB(5.2761) = 'Negative Binomial(5.2761)',
## NB(5.6641) = 'Negative Binomial(5.6641)',
## NB(5.703) = 'Negative Binomial(5.703)',
## NB(5.7771) = 'Negative Binomial(5.7771)',
## NB(6.3327) = 'Negative Binomial(6.3327)',
## NB(6.3887) = 'Negative Binomial(6.3887)'
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 2.511 0.4500
## m2 1.607 0.6159 0.2228
## m1 2.864 0.3884 0.10010
## m4 2.533 0.4780 0.1986 -0.5863
## m3 2.823 0.3909 0.13440 0.03028
## m9 2.118 0.5243 0.09018 0.2247
## m10 3.121 0.3745 0.10010 0.1993 -0.6247
## m11 2.093 0.5241 0.12230 0.2488 0.02861
## m12 3.123 0.3664 0.15910 0.2261 0.04766 -0.5659
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m2
## m1
## m4
## m3
## m9 0.12720
## m10 0.13240 -0.01064
## m11 0.09984
## m12 0.10010 -0.10130
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik
## m0 2 -165.387
## m2 3 -164.489
## m1 3 -164.599
## m4 4 -163.269
## m3 4 -164.522
## m9 5 -163.525
## m10 7 -162.149
## m11 -0.02581 7 -163.435
## m12 -0.03037 -0.06918 10 -162.045
## AICc delta weight
## m0 335.2 0.00 0.304
## m2 335.9 0.68 0.216
## m1 336.1 0.90 0.193
## m4 336.1 0.92 0.192
## m3 338.6 3.43 0.055
## m9 339.6 4.33 0.035
## m10 343.4 8.17 0.005
## m11 346.0 10.74 0.001
## m12 355.7 20.45 0.000
## Models ranked by AICc(x)
## Warning in matrix(value, n, p): data length [11] is not a sub-multiple or
## multiple of the number of columns [10]
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 2.511 0.4500
## m2 1.607 0.6159 0.2228
## m4 2.533 0.4779 0.1986 -0.5864
## m1 2.864 0.3884 0.10010
## m3 2.823 0.3909 0.13440 0.03027
## m9 2.117 0.5245 0.09022 0.2246
## m10 3.122 0.3745 0.10010 0.1992 -0.6249
## m11 2.092 0.5244 0.12230 0.2488 0.02862
## m12 3.123 0.3665 0.15880 0.2261 0.04745 -0.5665
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m2
## m4
## m1
## m3
## m9 0.12710
## m10 0.13240 -0.0107
## m11 0.09981
## m12 0.10010 -0.1004
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 family
## m0 NB(4.9638)
## m2 NB(5.2761)
## m4 NB(5.7771)
## m1 NB(5.237)
## m3 NB(5.2649)
## m9 NB(5.6641)
## m10 NB(6.3327)
## m11 -0.02578 NB(5.703)
## m12 -0.03037 -0.06857 NB(6.3887)
## init.theta df logLik AICc delta weight
## m0 4.96 3 -165.387 337.7 0.00 0.322
## m2 5.28 4 -164.462 338.5 0.83 0.213
## m4 5.78 5 -163.109 338.7 1.02 0.193
## m1 5.24 4 -164.578 338.8 1.06 0.189
## m3 5.26 5 -164.497 341.5 3.80 0.048
## m9 5.66 6 -163.403 342.5 4.76 0.030
## m10 6.33 8 -161.754 346.4 8.67 0.004
## m11 5.7 8 -163.300 349.5 11.76 0.001
## m12 6.39 11 -161.623 359.9 22.22 0.000
## Abbreviations:
## family: NB(4.9638) = 'Negative Binomial(4.9638)',
## NB(5.237) = 'Negative Binomial(5.237)',
## NB(5.2649) = 'Negative Binomial(5.2649)',
## NB(5.2761) = 'Negative Binomial(5.2761)',
## NB(5.6641) = 'Negative Binomial(5.6641)',
## NB(5.703) = 'Negative Binomial(5.703)',
## NB(5.7771) = 'Negative Binomial(5.7771)',
## NB(6.3327) = 'Negative Binomial(6.3327)',
## NB(6.3887) = 'Negative Binomial(6.3887)'
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 2.511 0.4500
## m2 1.607 0.6159 0.2228
## m1 2.864 0.3884 0.10010
## m4 2.533 0.4780 0.1986 -0.5863
## m3 2.823 0.3909 0.13440 0.03028
## m9 2.118 0.5243 0.09018 0.2247
## m10 3.121 0.3745 0.10010 0.1993 -0.6247
## m11 2.093 0.5241 0.12230 0.2488 0.02861
## m12 3.123 0.3664 0.15910 0.2261 0.04766 -0.5659
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m2
## m1
## m4
## m3
## m9 0.12720
## m10 0.13240 -0.01064
## m11 0.09984
## m12 0.10010 -0.10130
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik
## m0 2 -165.387
## m2 3 -164.489
## m1 3 -164.599
## m4 4 -163.269
## m3 4 -164.522
## m9 5 -163.525
## m10 7 -162.149
## m11 -0.02581 7 -163.435
## m12 -0.03037 -0.06918 10 -162.045
## AICc delta weight
## m0 335.2 0.00 0.304
## m2 335.9 0.68 0.216
## m1 336.1 0.90 0.193
## m4 336.1 0.92 0.192
## m3 338.6 3.43 0.055
## m9 339.6 4.33 0.035
## m10 343.4 8.17 0.005
## m11 346.0 10.74 0.001
## m12 355.7 20.45 0.000
## Models ranked by AICc(x)
## Warning in matrix(value, n, p): data length [11] is not a sub-multiple or
## multiple of the number of columns [10]
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 0.5714 0.9097
## m1 1.2490 0.7702 -0.1459
## m3 1.3430 0.7740 -0.3082 -0.13530
## m2 0.5382 0.9158 0.04296
## m4 0.4809 0.9463 0.04800 -0.3273
## m9 0.2169 0.9696 -0.1415 0.09068
## m11 0.3438 0.9681 -0.3129 0.05778 -0.14210
## m10 0.4487 0.9312 -0.2779 0.09620 -0.1285
## m12 1.7210 0.6943 -0.3871 0.02412 -0.07519 0.1268
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m1
## m3
## m2
## m4
## m9 0.2140
## m11 0.2452
## m10 0.2005 0.4818
## m12 0.2423 0.2210
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 family
## m0 NB(2.4468)
## m1 NB(2.5631)
## m3 NB(2.6994)
## m2 NB(2.4498)
## m4 NB(2.4803)
## m9 NB(2.6349)
## m11 0.02666 NB(2.7951)
## m10 NB(2.734)
## m12 0.05896 -0.258 NB(2.9725)
## init.theta df logLik AICc delta weight
## m0 2.45 3 -176.746 360.5 0.00 0.452
## m1 2.56 4 -176.001 361.7 1.22 0.246
## m3 2.7 5 -175.170 362.9 2.50 0.130
## m2 2.45 4 -176.727 363.1 2.67 0.119
## m4 2.48 5 -176.528 365.7 5.21 0.033
## m9 2.63 6 -175.562 366.9 6.49 0.018
## m11 2.8 8 -174.618 372.4 11.98 0.001
## m10 2.73 8 -174.974 373.1 12.69 0.001
## m12 2.97 11 -173.654 384.8 24.38 0.000
## Abbreviations:
## family: NB(2.4468) = 'Negative Binomial(2.4468)',
## NB(2.4498) = 'Negative Binomial(2.4498)',
## NB(2.4803) = 'Negative Binomial(2.4803)',
## NB(2.5631) = 'Negative Binomial(2.5631)',
## NB(2.6349) = 'Negative Binomial(2.6349)',
## NB(2.6994) = 'Negative Binomial(2.6994)',
## NB(2.734) = 'Negative Binomial(2.734)',
## NB(2.7951) = 'Negative Binomial(2.7951)',
## NB(2.9725) = 'Negative Binomial(2.9725)'
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 0.5714 0.9097
## m1 1.2480 0.7702 -0.1459
## m3 1.3420 0.7742 -0.3082 -0.13530
## m2 0.5382 0.9158 0.04295
## m4 0.4808 0.9463 0.04800 -0.3273
## m9 0.2172 0.9695 -0.1415 0.09067
## m11 0.3429 0.9682 -0.3130 0.05777 -0.14220
## m10 0.4482 0.9313 -0.2779 0.09621 -0.1285
## m12 1.7170 0.6951 -0.3876 0.02420 -0.07552 0.1260
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m1
## m3
## m2
## m4
## m9 0.2140
## m11 0.2451
## m10 0.2006 0.4819
## m12 0.2422 0.2222
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik
## m0 2 -176.746
## m1 3 -176.017
## m3 4 -175.242
## m2 3 -176.727
## m4 4 -176.530
## m9 5 -175.602
## m11 0.02660 7 -174.749
## m10 7 -175.065
## m12 0.05882 -0.2571 10 -173.928
## AICc delta weight
## m0 358.0 0.00 0.421
## m1 359.0 1.04 0.250
## m3 360.2 2.20 0.141
## m2 360.4 2.46 0.123
## m4 362.7 4.77 0.039
## m9 363.8 5.86 0.023
## m11 368.8 10.88 0.002
## m10 369.5 11.51 0.001
## m12 380.1 22.12 0.000
## Models ranked by AICc(x)
## Warning in matrix(value, n, p): data length [11] is not a sub-multiple or
## multiple of the number of columns [10]
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 0.5714 0.9097
## m1 1.2490 0.7702 -0.1459
## m3 1.3430 0.7740 -0.3082 -0.13530
## m2 0.5382 0.9158 0.04296
## m4 0.4809 0.9463 0.04800 -0.3273
## m9 0.2169 0.9696 -0.1415 0.09068
## m11 0.3438 0.9681 -0.3129 0.05778 -0.14210
## m10 0.4487 0.9312 -0.2779 0.09620 -0.1285
## m12 1.7210 0.6943 -0.3871 0.02412 -0.07519 0.1268
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m1
## m3
## m2
## m4
## m9 0.2140
## m11 0.2452
## m10 0.2005 0.4818
## m12 0.2423 0.2210
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 family
## m0 NB(2.4468)
## m1 NB(2.5631)
## m3 NB(2.6994)
## m2 NB(2.4498)
## m4 NB(2.4803)
## m9 NB(2.6349)
## m11 0.02666 NB(2.7951)
## m10 NB(2.734)
## m12 0.05896 -0.258 NB(2.9725)
## init.theta df logLik AICc delta weight
## m0 2.45 3 -176.746 360.5 0.00 0.452
## m1 2.56 4 -176.001 361.7 1.22 0.246
## m3 2.7 5 -175.170 362.9 2.50 0.130
## m2 2.45 4 -176.727 363.1 2.67 0.119
## m4 2.48 5 -176.528 365.7 5.21 0.033
## m9 2.63 6 -175.562 366.9 6.49 0.018
## m11 2.8 8 -174.618 372.4 11.98 0.001
## m10 2.73 8 -174.974 373.1 12.69 0.001
## m12 2.97 11 -173.654 384.8 24.38 0.000
## Abbreviations:
## family: NB(2.4468) = 'Negative Binomial(2.4468)',
## NB(2.4498) = 'Negative Binomial(2.4498)',
## NB(2.4803) = 'Negative Binomial(2.4803)',
## NB(2.5631) = 'Negative Binomial(2.5631)',
## NB(2.6349) = 'Negative Binomial(2.6349)',
## NB(2.6994) = 'Negative Binomial(2.6994)',
## NB(2.734) = 'Negative Binomial(2.734)',
## NB(2.7951) = 'Negative Binomial(2.7951)',
## NB(2.9725) = 'Negative Binomial(2.9725)'
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 0.5714 0.9097
## m1 1.2480 0.7702 -0.1459
## m3 1.3420 0.7742 -0.3082 -0.13530
## m2 0.5382 0.9158 0.04295
## m4 0.4808 0.9463 0.04800 -0.3273
## m9 0.2172 0.9695 -0.1415 0.09067
## m11 0.3429 0.9682 -0.3130 0.05777 -0.14220
## m10 0.4482 0.9313 -0.2779 0.09621 -0.1285
## m12 1.7170 0.6951 -0.3876 0.02420 -0.07552 0.1260
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m1
## m3
## m2
## m4
## m9 0.2140
## m11 0.2451
## m10 0.2006 0.4819
## m12 0.2422 0.2222
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik
## m0 2 -176.746
## m1 3 -176.017
## m3 4 -175.242
## m2 3 -176.727
## m4 4 -176.530
## m9 5 -175.602
## m11 0.02660 7 -174.749
## m10 7 -175.065
## m12 0.05882 -0.2571 10 -173.928
## AICc delta weight
## m0 358.0 0.00 0.421
## m1 359.0 1.04 0.250
## m3 360.2 2.20 0.141
## m2 360.4 2.46 0.123
## m4 362.7 4.77 0.039
## m9 363.8 5.86 0.023
## m11 368.8 10.88 0.002
## m10 369.5 11.51 0.001
## m12 380.1 22.12 0.000
## Models ranked by AICc(x)
## Warning in matrix(value, n, p): data length [11] is not a sub-multiple or
## multiple of the number of columns [10]
## Warning in glm.fitter(x = X, y = Y, w = w, start = start, etastart =
## etastart, : no observations informative at iteration 1
## Warning: glm.fit: algorithm did not converge
## Warning in matrix(value, n, p): data length [11] is not a sub-multiple or
## multiple of the number of columns [10]
## Model selection table
## (Int) log(mxv) cv.dpt prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m6 2.848 0.3885
## m3 3.403 0.2896 -0.9527
## m0 2.472 0.4239
## m4 3.346 0.2146 0.009958
## m1 3.226 0.3313 -0.003932
## m5 2.931 0.3558 -0.01044
## m2 2.653 0.3687 0.7593
## lst_wet family init.theta df logLik AICc delta weight
## m6 -0.01313 NB(4.2717) 4.27 4 -166.844 343.3 0.00 0.259
## m3 NB(4.2245) 4.22 4 -167.014 343.6 0.34 0.219
## m0 NB(3.7712) 3.77 3 -168.763 344.4 1.16 0.145
## m4 NB(4.1) 4.1 4 -167.473 344.5 1.26 0.138
## m1 NB(4.0775) 4.08 4 -167.557 344.7 1.42 0.127
## m5 NB(3.8935) 3.89 4 -168.267 346.1 2.85 0.062
## m2 NB(3.8381) 3.84 4 -168.490 346.6 3.29 0.050
## Abbreviations:
## family: NB(3.7712) = 'Negative Binomial(3.7712)',
## NB(3.8381) = 'Negative Binomial(3.8381)',
## NB(3.8935) = 'Negative Binomial(3.8935)',
## NB(4.0775) = 'Negative Binomial(4.0775)',
## NB(4.1) = 'Negative Binomial(4.1)',
## NB(4.2245) = 'Negative Binomial(4.2245)',
## NB(4.2717) = 'Negative Binomial(4.2717)'
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m6 2.849 0.3885
## m3 3.403 0.2895 -0.9527
## m4 3.345 0.2146 0.009956
## m1 3.226 -0.003932 0.3313
## m5 2.931 0.3558 -0.01044
## m2 2.653 0.3687 0.759
## lst_wet df logLik AICc delta weight
## m6 -0.01313 3 -166.844 340.6 0.00 0.307
## m3 3 -167.015 341.0 0.34 0.259
## m4 3 -167.485 341.9 1.28 0.162
## m1 3 -167.573 342.1 1.46 0.148
## m5 3 -168.333 343.6 2.98 0.069
## m2 3 -168.578 344.1 3.47 0.054
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) cv.dpt prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m6 2.848 0.3885
## m3 3.403 0.2896 -0.9527
## m0 2.472 0.4239
## m4 3.346 0.2146 0.009958
## m1 3.226 0.3313 -0.003932
## m5 2.931 0.3558 -0.01044
## m2 2.653 0.3687 0.7593
## lst_wet family init.theta df logLik AICc delta weight
## m6 -0.01313 NB(4.2717) 4.27 4 -166.844 343.3 0.00 0.259
## m3 NB(4.2245) 4.22 4 -167.014 343.6 0.34 0.219
## m0 NB(3.7712) 3.77 3 -168.763 344.4 1.16 0.145
## m4 NB(4.1) 4.1 4 -167.473 344.5 1.26 0.138
## m1 NB(4.0775) 4.08 4 -167.557 344.7 1.42 0.127
## m5 NB(3.8935) 3.89 4 -168.267 346.1 2.85 0.062
## m2 NB(3.8381) 3.84 4 -168.490 346.6 3.29 0.050
## Abbreviations:
## family: NB(3.7712) = 'Negative Binomial(3.7712)',
## NB(3.8381) = 'Negative Binomial(3.8381)',
## NB(3.8935) = 'Negative Binomial(3.8935)',
## NB(4.0775) = 'Negative Binomial(4.0775)',
## NB(4.1) = 'Negative Binomial(4.1)',
## NB(4.2245) = 'Negative Binomial(4.2245)',
## NB(4.2717) = 'Negative Binomial(4.2717)'
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m6 2.849 0.3885
## m3 3.403 0.2895 -0.9527
## m4 3.345 0.2146 0.009956
## m1 3.226 -0.003932 0.3313
## m5 2.931 0.3558 -0.01044
## m2 2.653 0.3687 0.759
## lst_wet df logLik AICc delta weight
## m6 -0.01313 3 -166.844 340.6 0.00 0.307
## m3 3 -167.015 341.0 0.34 0.259
## m4 3 -167.485 341.9 1.28 0.162
## m1 3 -167.573 342.1 1.46 0.148
## m5 3 -168.333 343.6 2.98 0.069
## m2 3 -168.578 344.1 3.47 0.054
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) cv.dpt prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m6 2.848 0.3885
## m3 3.403 0.2896 -0.9527
## m0 2.472 0.4239
## m4 3.346 0.2146 0.009958
## m1 3.226 0.3313 -0.003932
## m5 2.931 0.3558 -0.01044
## m2 2.653 0.3687 0.7593
## lst_wet family init.theta df logLik AICc delta weight
## m6 -0.01313 NB(4.2717) 4.27 4 -166.844 343.3 0.00 0.259
## m3 NB(4.2245) 4.22 4 -167.014 343.6 0.34 0.219
## m0 NB(3.7712) 3.77 3 -168.763 344.4 1.16 0.145
## m4 NB(4.1) 4.1 4 -167.473 344.5 1.26 0.138
## m1 NB(4.0775) 4.08 4 -167.557 344.7 1.42 0.127
## m5 NB(3.8935) 3.89 4 -168.267 346.1 2.85 0.062
## m2 NB(3.8381) 3.84 4 -168.490 346.6 3.29 0.050
## Abbreviations:
## family: NB(3.7712) = 'Negative Binomial(3.7712)',
## NB(3.8381) = 'Negative Binomial(3.8381)',
## NB(3.8935) = 'Negative Binomial(3.8935)',
## NB(4.0775) = 'Negative Binomial(4.0775)',
## NB(4.1) = 'Negative Binomial(4.1)',
## NB(4.2245) = 'Negative Binomial(4.2245)',
## NB(4.2717) = 'Negative Binomial(4.2717)'
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m6 2.849 0.3885
## m3 3.403 0.2895 -0.9527
## m4 3.345 0.2146 0.009956
## m1 3.226 -0.003932 0.3313
## m5 2.931 0.3558 -0.01044
## m2 2.653 0.3687 0.759
## lst_wet df logLik AICc delta weight
## m6 -0.01313 3 -166.844 340.6 0.00 0.307
## m3 3 -167.015 341.0 0.34 0.259
## m4 3 -167.485 341.9 1.28 0.162
## m1 3 -167.573 342.1 1.46 0.148
## m5 3 -168.333 343.6 2.98 0.069
## m2 3 -168.578 344.1 3.47 0.054
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) cv.dpt prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m6 2.848 0.3885
## m3 3.403 0.2896 -0.9527
## m0 2.472 0.4239
## m4 3.346 0.2146 0.009958
## m1 3.226 0.3313 -0.003932
## m5 2.931 0.3558 -0.01044
## m2 2.653 0.3687 0.7593
## lst_wet family init.theta df logLik AICc delta weight
## m6 -0.01313 NB(4.2717) 4.27 4 -166.844 343.3 0.00 0.259
## m3 NB(4.2245) 4.22 4 -167.014 343.6 0.34 0.219
## m0 NB(3.7712) 3.77 3 -168.763 344.4 1.16 0.145
## m4 NB(4.1) 4.1 4 -167.473 344.5 1.26 0.138
## m1 NB(4.0775) 4.08 4 -167.557 344.7 1.42 0.127
## m5 NB(3.8935) 3.89 4 -168.267 346.1 2.85 0.062
## m2 NB(3.8381) 3.84 4 -168.490 346.6 3.29 0.050
## Abbreviations:
## family: NB(3.7712) = 'Negative Binomial(3.7712)',
## NB(3.8381) = 'Negative Binomial(3.8381)',
## NB(3.8935) = 'Negative Binomial(3.8935)',
## NB(4.0775) = 'Negative Binomial(4.0775)',
## NB(4.1) = 'Negative Binomial(4.1)',
## NB(4.2245) = 'Negative Binomial(4.2245)',
## NB(4.2717) = 'Negative Binomial(4.2717)'
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m6 2.849 0.3885
## m3 3.403 0.2895 -0.9527
## m4 3.345 0.2146 0.009956
## m1 3.226 -0.003932 0.3313
## m5 2.931 0.3558 -0.01044
## m2 2.653 0.3687 0.759
## lst_wet df logLik AICc delta weight
## m6 -0.01313 3 -166.844 340.6 0.00 0.307
## m3 3 -167.015 341.0 0.34 0.259
## m4 3 -167.485 341.9 1.28 0.162
## m1 3 -167.573 342.1 1.46 0.148
## m5 3 -168.333 343.6 2.98 0.069
## m2 3 -168.578 344.1 3.47 0.054
## Models ranked by AICc(x)
## Warning in matrix(value, n, p): data length [11] is not a sub-multiple or
## multiple of the number of columns [10]
## Model selection table
## (Int) log(mxv) cv.dpt prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m6 2.848 0.3885
## m3 3.403 0.2896 -0.9527
## m0 2.472 0.4239
## m4 3.346 0.2146 0.009958
## m1 3.226 0.3313 -0.003932
## m5 2.931 0.3558 -0.01044
## m2 2.653 0.3687 0.7593
## lst_wet family init.theta df logLik AICc delta weight
## m6 -0.01313 NB(4.2717) 4.27 4 -166.844 343.3 0.00 0.259
## m3 NB(4.2245) 4.22 4 -167.014 343.6 0.34 0.219
## m0 NB(3.7712) 3.77 3 -168.763 344.4 1.16 0.145
## m4 NB(4.1) 4.1 4 -167.473 344.5 1.26 0.138
## m1 NB(4.0775) 4.08 4 -167.557 344.7 1.42 0.127
## m5 NB(3.8935) 3.89 4 -168.267 346.1 2.85 0.062
## m2 NB(3.8381) 3.84 4 -168.490 346.6 3.29 0.050
## Abbreviations:
## family: NB(3.7712) = 'Negative Binomial(3.7712)',
## NB(3.8381) = 'Negative Binomial(3.8381)',
## NB(3.8935) = 'Negative Binomial(3.8935)',
## NB(4.0775) = 'Negative Binomial(4.0775)',
## NB(4.1) = 'Negative Binomial(4.1)',
## NB(4.2245) = 'Negative Binomial(4.2245)',
## NB(4.2717) = 'Negative Binomial(4.2717)'
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m6 2.849 0.3885
## m3 3.403 0.2895 -0.9527
## m4 3.345 0.2146 0.009956
## m1 3.226 -0.003932 0.3313
## m5 2.931 0.3558 -0.01044
## m2 2.653 0.3687 0.759
## lst_wet df logLik AICc delta weight
## m6 -0.01313 3 -166.844 340.6 0.00 0.307
## m3 3 -167.015 341.0 0.34 0.259
## m4 3 -167.485 341.9 1.28 0.162
## m1 3 -167.573 342.1 1.46 0.148
## m5 3 -168.333 343.6 2.98 0.069
## m2 3 -168.578 344.1 3.47 0.054
## Models ranked by AICc(x)
## Warning in matrix(value, n, p): data length [11] is not a sub-multiple or
## multiple of the number of columns [10]
## Model selection table
## (Int) log(mxv) cv.dpt prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m5 11.50 -0.9153 0.0332
## m4 11.08 -0.7613 -0.00902
## m1 11.74 -0.9655 0.004374
## m3 11.38 -0.8953 1.034
## m2 10.11 -0.6674 -4.009
## m0 10.89 -0.8151
## m6 11.12 -0.8732
## lst_wet family init.theta df logLik AICc delta weight
## m5 NB(5.8547) 5.85 4 -171.889 353.4 0.00 0.413
## m4 NB(5.6063) 5.61 4 -172.547 354.7 1.32 0.214
## m1 NB(5.3678) 5.37 4 -173.227 356.1 2.68 0.108
## m3 NB(5.3412) 5.34 4 -173.302 356.2 2.83 0.101
## m2 NB(5.2651) 5.27 4 -173.515 356.6 3.25 0.081
## m0 NB(4.7023) 4.7 3 -175.288 357.5 4.12 0.053
## m6 0.005257 NB(4.9464) 4.95 4 -174.494 358.6 5.21 0.031
## Abbreviations:
## family: NB(4.7023) = 'Negative Binomial(4.7023)',
## NB(4.9464) = 'Negative Binomial(4.9464)',
## NB(5.2651) = 'Negative Binomial(5.2651)',
## NB(5.3412) = 'Negative Binomial(5.3412)',
## NB(5.3678) = 'Negative Binomial(5.3678)',
## NB(5.6063) = 'Negative Binomial(5.6063)',
## NB(5.8547) = 'Negative Binomial(5.8547)'
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m5 11.50 -0.9153 0.0332
## m4 11.08 -0.7612 -0.00902
## m1 11.74 0.004375 -0.9654
## m3 11.38 -0.8952 1.034
## m2 10.11 -0.6673 -4.009
## m6 11.12 -0.8728
## lst_wet df logLik AICc delta weight
## m5 3 -171.889 350.7 0.00 0.449
## m4 3 -172.561 352.0 1.34 0.229
## m1 3 -173.285 353.5 2.79 0.111
## m3 3 -173.366 353.7 2.95 0.103
## m2 3 -173.602 354.1 3.43 0.081
## m6 0.005258 3 -174.719 356.4 5.66 0.026
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) cv.dpt prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m5 11.50 -0.9153 0.0332
## m4 11.08 -0.7613 -0.00902
## m1 11.74 -0.9655 0.004374
## m3 11.38 -0.8953 1.034
## m2 10.11 -0.6674 -4.009
## m0 10.89 -0.8151
## m6 11.12 -0.8732
## lst_wet family init.theta df logLik AICc delta weight
## m5 NB(5.8547) 5.85 4 -171.889 353.4 0.00 0.413
## m4 NB(5.6063) 5.61 4 -172.547 354.7 1.32 0.214
## m1 NB(5.3678) 5.37 4 -173.227 356.1 2.68 0.108
## m3 NB(5.3412) 5.34 4 -173.302 356.2 2.83 0.101
## m2 NB(5.2651) 5.27 4 -173.515 356.6 3.25 0.081
## m0 NB(4.7023) 4.7 3 -175.288 357.5 4.12 0.053
## m6 0.005257 NB(4.9464) 4.95 4 -174.494 358.6 5.21 0.031
## Abbreviations:
## family: NB(4.7023) = 'Negative Binomial(4.7023)',
## NB(4.9464) = 'Negative Binomial(4.9464)',
## NB(5.2651) = 'Negative Binomial(5.2651)',
## NB(5.3412) = 'Negative Binomial(5.3412)',
## NB(5.3678) = 'Negative Binomial(5.3678)',
## NB(5.6063) = 'Negative Binomial(5.6063)',
## NB(5.8547) = 'Negative Binomial(5.8547)'
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m5 11.50 -0.9153 0.0332
## m4 11.08 -0.7612 -0.00902
## m1 11.74 0.004375 -0.9654
## m3 11.38 -0.8952 1.034
## m2 10.11 -0.6673 -4.009
## m6 11.12 -0.8728
## lst_wet df logLik AICc delta weight
## m5 3 -171.889 350.7 0.00 0.449
## m4 3 -172.561 352.0 1.34 0.229
## m1 3 -173.285 353.5 2.79 0.111
## m3 3 -173.366 353.7 2.95 0.103
## m2 3 -173.602 354.1 3.43 0.081
## m6 0.005258 3 -174.719 356.4 5.66 0.026
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) cv.dpt prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m5 11.50 -0.9153 0.0332
## m4 11.08 -0.7613 -0.00902
## m1 11.74 -0.9655 0.004374
## m3 11.38 -0.8953 1.034
## m2 10.11 -0.6674 -4.009
## m0 10.89 -0.8151
## m6 11.12 -0.8732
## lst_wet family init.theta df logLik AICc delta weight
## m5 NB(5.8547) 5.85 4 -171.889 353.4 0.00 0.413
## m4 NB(5.6063) 5.61 4 -172.547 354.7 1.32 0.214
## m1 NB(5.3678) 5.37 4 -173.227 356.1 2.68 0.108
## m3 NB(5.3412) 5.34 4 -173.302 356.2 2.83 0.101
## m2 NB(5.2651) 5.27 4 -173.515 356.6 3.25 0.081
## m0 NB(4.7023) 4.7 3 -175.288 357.5 4.12 0.053
## m6 0.005257 NB(4.9464) 4.95 4 -174.494 358.6 5.21 0.031
## Abbreviations:
## family: NB(4.7023) = 'Negative Binomial(4.7023)',
## NB(4.9464) = 'Negative Binomial(4.9464)',
## NB(5.2651) = 'Negative Binomial(5.2651)',
## NB(5.3412) = 'Negative Binomial(5.3412)',
## NB(5.3678) = 'Negative Binomial(5.3678)',
## NB(5.6063) = 'Negative Binomial(5.6063)',
## NB(5.8547) = 'Negative Binomial(5.8547)'
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m5 11.50 -0.9153 0.0332
## m4 11.08 -0.7612 -0.00902
## m1 11.74 0.004375 -0.9654
## m3 11.38 -0.8952 1.034
## m2 10.11 -0.6673 -4.009
## m6 11.12 -0.8728
## lst_wet df logLik AICc delta weight
## m5 3 -171.889 350.7 0.00 0.449
## m4 3 -172.561 352.0 1.34 0.229
## m1 3 -173.285 353.5 2.79 0.111
## m3 3 -173.366 353.7 2.95 0.103
## m2 3 -173.602 354.1 3.43 0.081
## m6 0.005258 3 -174.719 356.4 5.66 0.026
## Models ranked by AICc(x)
## Warning in matrix(value, n, p): data length [11] is not a sub-multiple or
## multiple of the number of columns [10]
## Model selection table
## (Int) log(mxv) cv.dpt prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m5 11.50 -0.9153 0.0332
## m4 11.08 -0.7613 -0.00902
## m1 11.74 -0.9655 0.004374
## m3 11.38 -0.8953 1.034
## m2 10.11 -0.6674 -4.009
## m0 10.89 -0.8151
## m6 11.12 -0.8732
## lst_wet family init.theta df logLik AICc delta weight
## m5 NB(5.8547) 5.85 4 -171.889 353.4 0.00 0.413
## m4 NB(5.6063) 5.61 4 -172.547 354.7 1.32 0.214
## m1 NB(5.3678) 5.37 4 -173.227 356.1 2.68 0.108
## m3 NB(5.3412) 5.34 4 -173.302 356.2 2.83 0.101
## m2 NB(5.2651) 5.27 4 -173.515 356.6 3.25 0.081
## m0 NB(4.7023) 4.7 3 -175.288 357.5 4.12 0.053
## m6 0.005257 NB(4.9464) 4.95 4 -174.494 358.6 5.21 0.031
## Abbreviations:
## family: NB(4.7023) = 'Negative Binomial(4.7023)',
## NB(4.9464) = 'Negative Binomial(4.9464)',
## NB(5.2651) = 'Negative Binomial(5.2651)',
## NB(5.3412) = 'Negative Binomial(5.3412)',
## NB(5.3678) = 'Negative Binomial(5.3678)',
## NB(5.6063) = 'Negative Binomial(5.6063)',
## NB(5.8547) = 'Negative Binomial(5.8547)'
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m5 11.50 -0.9153 0.0332
## m4 11.08 -0.7612 -0.00902
## m1 11.74 0.004375 -0.9654
## m3 11.38 -0.8952 1.034
## m2 10.11 -0.6673 -4.009
## m6 11.12 -0.8728
## lst_wet df logLik AICc delta weight
## m5 3 -171.889 350.7 0.00 0.449
## m4 3 -172.561 352.0 1.34 0.229
## m1 3 -173.285 353.5 2.79 0.111
## m3 3 -173.366 353.7 2.95 0.103
## m2 3 -173.602 354.1 3.43 0.081
## m6 0.005258 3 -174.719 356.4 5.66 0.026
## Models ranked by AICc(x)
## Warning in matrix(value, n, p): data length [11] is not a sub-multiple or
## multiple of the number of columns [10]
## Model selection table
## (Int) log(mxv) cv.dpt prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m4 4.327 -0.01425 0.01396
## m0 2.511 0.45000
## m5 3.054 0.35720 -0.04167
## m3 3.099 0.34800 -1.875
## m1 3.738 0.25320 -0.006452
## m6 2.964 0.37500
## m2 2.536 0.44380 0.03727
## lst_wet family init.theta df logLik AICc delta weight
## m4 NB(5.5062) 5.51 4 -163.820 337.2 0.00 0.299
## m0 NB(4.9638) 4.96 3 -165.387 337.7 0.46 0.238
## m5 NB(5.1453) 5.15 4 -164.839 339.3 2.04 0.108
## m3 NB(5.1272) 5.13 4 -164.894 339.4 2.15 0.102
## m1 NB(5.1176) 5.12 4 -164.925 339.4 2.21 0.099
## m6 -0.01035 NB(5.0915) 5.09 4 -165.000 339.6 2.36 0.092
## m2 NB(4.9648) 4.96 4 -165.384 340.4 3.13 0.063
## Abbreviations:
## family: NB(4.9638) = 'Negative Binomial(4.9638)',
## NB(4.9648) = 'Negative Binomial(4.9648)',
## NB(5.0915) = 'Negative Binomial(5.0915)',
## NB(5.1176) = 'Negative Binomial(5.1176)',
## NB(5.1272) = 'Negative Binomial(5.1272)',
## NB(5.1453) = 'Negative Binomial(5.1453)',
## NB(5.5062) = 'Negative Binomial(5.5062)'
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m4 4.327 -0.01427 0.01396
## m5 3.054 0.35720 -0.04166
## m3 3.099 0.34800 -1.875
## m1 3.738 -0.006452 0.25330
## m6 2.964 0.37500
## m2 2.536 0.44380 0.03738
## lst_wet df logLik AICc delta weight
## m4 3 -163.820 334.6 0.00 0.403
## m5 3 -164.874 336.7 2.11 0.140
## m3 3 -164.933 336.8 2.23 0.132
## m1 3 -164.965 336.9 2.29 0.128
## m6 -0.01035 3 -165.046 337.0 2.45 0.118
## m2 3 -165.466 337.9 3.29 0.078
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) cv.dpt prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m4 4.327 -0.01425 0.01396
## m0 2.511 0.45000
## m5 3.054 0.35720 -0.04167
## m3 3.099 0.34800 -1.875
## m1 3.738 0.25320 -0.006452
## m6 2.964 0.37500
## m2 2.536 0.44380 0.03727
## lst_wet family init.theta df logLik AICc delta weight
## m4 NB(5.5062) 5.51 4 -163.820 337.2 0.00 0.299
## m0 NB(4.9638) 4.96 3 -165.387 337.7 0.46 0.238
## m5 NB(5.1453) 5.15 4 -164.839 339.3 2.04 0.108
## m3 NB(5.1272) 5.13 4 -164.894 339.4 2.15 0.102
## m1 NB(5.1176) 5.12 4 -164.925 339.4 2.21 0.099
## m6 -0.01035 NB(5.0915) 5.09 4 -165.000 339.6 2.36 0.092
## m2 NB(4.9648) 4.96 4 -165.384 340.4 3.13 0.063
## Abbreviations:
## family: NB(4.9638) = 'Negative Binomial(4.9638)',
## NB(4.9648) = 'Negative Binomial(4.9648)',
## NB(5.0915) = 'Negative Binomial(5.0915)',
## NB(5.1176) = 'Negative Binomial(5.1176)',
## NB(5.1272) = 'Negative Binomial(5.1272)',
## NB(5.1453) = 'Negative Binomial(5.1453)',
## NB(5.5062) = 'Negative Binomial(5.5062)'
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m4 4.327 -0.01427 0.01396
## m5 3.054 0.35720 -0.04166
## m3 3.099 0.34800 -1.875
## m1 3.738 -0.006452 0.25330
## m6 2.964 0.37500
## m2 2.536 0.44380 0.03738
## lst_wet df logLik AICc delta weight
## m4 3 -163.820 334.6 0.00 0.403
## m5 3 -164.874 336.7 2.11 0.140
## m3 3 -164.933 336.8 2.23 0.132
## m1 3 -164.965 336.9 2.29 0.128
## m6 -0.01035 3 -165.046 337.0 2.45 0.118
## m2 3 -165.466 337.9 3.29 0.078
## Models ranked by AICc(x)
## Warning in matrix(value, n, p): data length [11] is not a sub-multiple or
## multiple of the number of columns [10]
## Model selection table
## (Int) log(mxv) cv.dpt prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m4 4.327 -0.01425 0.01396
## m0 2.511 0.45000
## m5 3.054 0.35720 -0.04167
## m3 3.099 0.34800 -1.875
## m1 3.738 0.25320 -0.006452
## m6 2.964 0.37500
## m2 2.536 0.44380 0.03727
## lst_wet family init.theta df logLik AICc delta weight
## m4 NB(5.5062) 5.51 4 -163.820 337.2 0.00 0.299
## m0 NB(4.9638) 4.96 3 -165.387 337.7 0.46 0.238
## m5 NB(5.1453) 5.15 4 -164.839 339.3 2.04 0.108
## m3 NB(5.1272) 5.13 4 -164.894 339.4 2.15 0.102
## m1 NB(5.1176) 5.12 4 -164.925 339.4 2.21 0.099
## m6 -0.01035 NB(5.0915) 5.09 4 -165.000 339.6 2.36 0.092
## m2 NB(4.9648) 4.96 4 -165.384 340.4 3.13 0.063
## Abbreviations:
## family: NB(4.9638) = 'Negative Binomial(4.9638)',
## NB(4.9648) = 'Negative Binomial(4.9648)',
## NB(5.0915) = 'Negative Binomial(5.0915)',
## NB(5.1176) = 'Negative Binomial(5.1176)',
## NB(5.1272) = 'Negative Binomial(5.1272)',
## NB(5.1453) = 'Negative Binomial(5.1453)',
## NB(5.5062) = 'Negative Binomial(5.5062)'
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m4 4.327 -0.01427 0.01396
## m5 3.054 0.35720 -0.04166
## m3 3.099 0.34800 -1.875
## m1 3.738 -0.006452 0.25330
## m6 2.964 0.37500
## m2 2.536 0.44380 0.03738
## lst_wet df logLik AICc delta weight
## m4 3 -163.820 334.6 0.00 0.403
## m5 3 -164.874 336.7 2.11 0.140
## m3 3 -164.933 336.8 2.23 0.132
## m1 3 -164.965 336.9 2.29 0.128
## m6 -0.01035 3 -165.046 337.0 2.45 0.118
## m2 3 -165.466 337.9 3.29 0.078
## Models ranked by AICc(x)
## Warning in matrix(value, n, p): data length [11] is not a sub-multiple or
## multiple of the number of columns [10]
## Model selection table
## (Int) log(mxv) cv.dpt prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m2 -0.9354 1.1120 2.176
## m0 0.5714 0.9097
## m6 0.2714 0.9936
## m1 1.3380 0.8081 -0.004148
## m5 1.5780 0.7337 -0.07
## m3 1.1900 0.8020 -0.3132
## m4 0.1506 1.0170 -0.007061
## lst_wet family init.theta df logLik AICc delta weight
## m2 NB(2.8792) 2.88 4 -174.149 358.0 0.00 0.545
## m0 NB(2.4468) 2.45 3 -176.746 360.5 2.49 0.157
## m6 -0.009609 NB(2.5757) 2.58 4 -175.920 361.5 3.54 0.093
## m1 NB(2.5015) 2.5 4 -176.388 362.4 4.48 0.058
## m5 NB(2.4925) 2.49 4 -176.450 362.6 4.60 0.055
## m3 NB(2.4673) 2.47 4 -176.611 362.9 4.92 0.047
## m4 NB(2.4641) 2.46 4 -176.634 362.9 4.97 0.045
## Abbreviations:
## family: NB(2.4468) = 'Negative Binomial(2.4468)',
## NB(2.4641) = 'Negative Binomial(2.4641)',
## NB(2.4673) = 'Negative Binomial(2.4673)',
## NB(2.4925) = 'Negative Binomial(2.4925)',
## NB(2.5015) = 'Negative Binomial(2.5015)',
## NB(2.5757) = 'Negative Binomial(2.5757)',
## NB(2.8792) = 'Negative Binomial(2.8792)'
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m2 -0.9354 1.1120 2.176
## m6 0.2726 0.9933
## m1 1.3390 -0.004144 0.8078
## m5 1.5780 0.7337 -0.07002
## m3 1.1910 0.8019 -0.3129
## m4 0.1502 1.0170 -0.007065
## lst_wet df logLik AICc delta weight
## m2 3 -174.149 355.3 0.00 0.682
## m6 -0.009607 3 -176.022 359.0 3.74 0.105
## m1 3 -176.551 360.1 4.80 0.062
## m5 3 -176.622 360.2 4.95 0.058
## m3 3 -176.809 360.6 5.32 0.048
## m4 3 -176.836 360.6 5.37 0.046
## Models ranked by AICc(x)
## Warning in matrix(value, n, p): data length [11] is not a sub-multiple or
## multiple of the number of columns [10]
## Model selection table
## (Int) log(mxv) cv.dpt prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m2 -0.9354 1.1120 2.176
## m0 0.5714 0.9097
## m6 0.2714 0.9936
## m1 1.3380 0.8081 -0.004148
## m5 1.5780 0.7337 -0.07
## m3 1.1900 0.8020 -0.3132
## m4 0.1506 1.0170 -0.007061
## lst_wet family init.theta df logLik AICc delta weight
## m2 NB(2.8792) 2.88 4 -174.149 358.0 0.00 0.545
## m0 NB(2.4468) 2.45 3 -176.746 360.5 2.49 0.157
## m6 -0.009609 NB(2.5757) 2.58 4 -175.920 361.5 3.54 0.093
## m1 NB(2.5015) 2.5 4 -176.388 362.4 4.48 0.058
## m5 NB(2.4925) 2.49 4 -176.450 362.6 4.60 0.055
## m3 NB(2.4673) 2.47 4 -176.611 362.9 4.92 0.047
## m4 NB(2.4641) 2.46 4 -176.634 362.9 4.97 0.045
## Abbreviations:
## family: NB(2.4468) = 'Negative Binomial(2.4468)',
## NB(2.4641) = 'Negative Binomial(2.4641)',
## NB(2.4673) = 'Negative Binomial(2.4673)',
## NB(2.4925) = 'Negative Binomial(2.4925)',
## NB(2.5015) = 'Negative Binomial(2.5015)',
## NB(2.5757) = 'Negative Binomial(2.5757)',
## NB(2.8792) = 'Negative Binomial(2.8792)'
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m2 -0.9354 1.1120 2.176
## m6 0.2726 0.9933
## m1 1.3390 -0.004144 0.8078
## m5 1.5780 0.7337 -0.07002
## m3 1.1910 0.8019 -0.3129
## m4 0.1502 1.0170 -0.007065
## lst_wet df logLik AICc delta weight
## m2 3 -174.149 355.3 0.00 0.682
## m6 -0.009607 3 -176.022 359.0 3.74 0.105
## m1 3 -176.551 360.1 4.80 0.062
## m5 3 -176.622 360.2 4.95 0.058
## m3 3 -176.809 360.6 5.32 0.048
## m4 3 -176.836 360.6 5.37 0.046
## Models ranked by AICc(x)
## Warning in matrix(value, n, p): data length [11] is not a sub-multiple or
## multiple of the number of columns [10]
## Warning in glm.fitter(x = X, y = Y, w = w, start = start, etastart =
## etastart, : no observations informative at iteration 1
## Warning: glm.fit: algorithm did not converge
## Warning in matrix(value, n, p): data length [11] is not a sub-multiple or
## multiple of the number of columns [10]
## Error in nrow(concord.out): object 'concord.out' not found
sites<-c("puertorico", "macae", "frenchguiana", "costarica", "colombia", "argentina")
no126data$sqrt.n15.bromeliad.final<-(no126data$n15.bromeliad.final+4)^0.5
concord.out9<-concord.magic(sites, "sqrt.n15.bromeliad.final", no126data, 10, 2, "gaussian")
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m1 5.356 -0.47040 0.2403
## m0 6.698 -0.71020
## m3 4.278 -0.31020 0.4169 0.1572
## m2 6.395 -0.65840 0.17500
## m4 6.324 -0.61870 0.17710 -0.5022
## m10 2.331 0.09878 0.7325 0.17120 -0.9184
## m9 4.810 -0.37680 0.2451 0.14980
## m11 3.803 -0.22930 0.4252 0.08946 0.1611
## m12 -1.836 0.74100 1.3550 0.16880 0.4979 -0.1919
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m1
## m0
## m3
## m2
## m4
## m10 -0.17870 -1.446
## m9 -0.16360
## m11 -0.08959
## m12 -0.16250 -2.592
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik AICc
## m1 4 -34.677 79.0
## m0 3 -36.151 79.2
## m3 5 -33.976 80.5
## m2 4 -35.928 81.5
## m4 5 -35.624 83.7
## m10 8 -30.475 83.8
## m9 6 -34.144 83.9
## m11 0.07349 8 -33.316 89.5
## m12 0.03429 -0.9057 11 -27.579 91.8
## delta weight
## m1 0.00 0.345
## m0 0.27 0.301
## m3 1.50 0.163
## m2 2.50 0.099
## m4 4.79 0.031
## m10 4.85 0.030
## m9 4.99 0.028
## m11 10.54 0.002
## m12 12.87 0.001
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m1 5.356 -0.47040 0.2403
## m0 6.698 -0.71020
## m3 4.278 -0.31020 0.4169 0.1572
## m2 6.395 -0.65840 0.17500
## m4 6.324 -0.61870 0.17710 -0.5022
## m10 2.331 0.09878 0.7325 0.17120 -0.9184
## m9 4.810 -0.37680 0.2451 0.14980
## m11 3.803 -0.22930 0.4252 0.08946 0.1611
## m12 -1.836 0.74100 1.3550 0.16880 0.4979 -0.1919
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m1
## m0
## m3
## m2
## m4
## m10 -0.17870 -1.446
## m9 -0.16360
## m11 -0.08959
## m12 -0.16250 -2.592
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik AICc
## m1 4 -34.677 79.0
## m0 3 -36.151 79.2
## m3 5 -33.976 80.5
## m2 4 -35.928 81.5
## m4 5 -35.624 83.7
## m10 8 -30.475 83.8
## m9 6 -34.144 83.9
## m11 0.07349 8 -33.316 89.5
## m12 0.03429 -0.9057 11 -27.579 91.8
## delta weight
## m1 0.00 0.345
## m0 0.27 0.301
## m3 1.50 0.163
## m2 2.50 0.099
## m4 4.79 0.031
## m10 4.85 0.030
## m9 4.99 0.028
## m11 10.54 0.002
## m12 12.87 0.001
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m1 5.356 -0.47040 0.2403
## m0 6.698 -0.71020
## m3 4.278 -0.31020 0.4169 0.1572
## m2 6.395 -0.65840 0.17500
## m4 6.324 -0.61870 0.17710 -0.5022
## m10 2.331 0.09878 0.7325 0.17120 -0.9184
## m9 4.810 -0.37680 0.2451 0.14980
## m11 3.803 -0.22930 0.4252 0.08946 0.1611
## m12 -1.836 0.74100 1.3550 0.16880 0.4979 -0.1919
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m1
## m0
## m3
## m2
## m4
## m10 -0.17870 -1.446
## m9 -0.16360
## m11 -0.08959
## m12 -0.16250 -2.592
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik AICc
## m1 4 -34.677 79.0
## m0 3 -36.151 79.2
## m3 5 -33.976 80.5
## m2 4 -35.928 81.5
## m4 5 -35.624 83.7
## m10 8 -30.475 83.8
## m9 6 -34.144 83.9
## m11 0.07349 8 -33.316 89.5
## m12 0.03429 -0.9057 11 -27.579 91.8
## delta weight
## m1 0.00 0.345
## m0 0.27 0.301
## m3 1.50 0.163
## m2 2.50 0.099
## m4 4.79 0.031
## m10 4.85 0.030
## m9 4.99 0.028
## m11 10.54 0.002
## m12 12.87 0.001
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m1 5.356 -0.47040 0.2403
## m0 6.698 -0.71020
## m3 4.278 -0.31020 0.4169 0.1572
## m2 6.395 -0.65840 0.17500
## m4 6.324 -0.61870 0.17710 -0.5022
## m10 2.331 0.09878 0.7325 0.17120 -0.9184
## m9 4.810 -0.37680 0.2451 0.14980
## m11 3.803 -0.22930 0.4252 0.08946 0.1611
## m12 -1.836 0.74100 1.3550 0.16880 0.4979 -0.1919
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m1
## m0
## m3
## m2
## m4
## m10 -0.17870 -1.446
## m9 -0.16360
## m11 -0.08959
## m12 -0.16250 -2.592
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik AICc
## m1 4 -34.677 79.0
## m0 3 -36.151 79.2
## m3 5 -33.976 80.5
## m2 4 -35.928 81.5
## m4 5 -35.624 83.7
## m10 8 -30.475 83.8
## m9 6 -34.144 83.9
## m11 0.07349 8 -33.316 89.5
## m12 0.03429 -0.9057 11 -27.579 91.8
## delta weight
## m1 0.00 0.345
## m0 0.27 0.301
## m3 1.50 0.163
## m2 2.50 0.099
## m4 4.79 0.031
## m10 4.85 0.030
## m9 4.99 0.028
## m11 10.54 0.002
## m12 12.87 0.001
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m1 5.356 -0.47040 0.2403
## m0 6.698 -0.71020
## m3 4.278 -0.31020 0.4169 0.1572
## m2 6.395 -0.65840 0.17500
## m4 6.324 -0.61870 0.17710 -0.5022
## m10 2.331 0.09878 0.7325 0.17120 -0.9184
## m9 4.810 -0.37680 0.2451 0.14980
## m11 3.803 -0.22930 0.4252 0.08946 0.1611
## m12 -1.836 0.74100 1.3550 0.16880 0.4979 -0.1919
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m1
## m0
## m3
## m2
## m4
## m10 -0.17870 -1.446
## m9 -0.16360
## m11 -0.08959
## m12 -0.16250 -2.592
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik AICc
## m1 4 -34.677 79.0
## m0 3 -36.151 79.2
## m3 5 -33.976 80.5
## m2 4 -35.928 81.5
## m4 5 -35.624 83.7
## m10 8 -30.475 83.8
## m9 6 -34.144 83.9
## m11 0.07349 8 -33.316 89.5
## m12 0.03429 -0.9057 11 -27.579 91.8
## delta weight
## m1 0.00 0.345
## m0 0.27 0.301
## m3 1.50 0.163
## m2 2.50 0.099
## m4 4.79 0.031
## m10 4.85 0.030
## m9 4.99 0.028
## m11 10.54 0.002
## m12 12.87 0.001
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 24.250 -2.361
## m1 16.320 -1.236 -0.63400
## m2 29.120 -3.068 0.5355
## m3 17.160 -1.348 -0.70270 -0.06662
## m9 16.050 -1.197 -0.63560 0.1607
## m4 36.520 -4.070 0.6074 -1.546
## m10 22.350 -2.028 0.09693 0.2321 -1.808
## m11 14.980 -1.031 -0.70650 -0.2313 -0.06561
## m12 -1.864 1.327 1.08400 -0.4493 0.97940 1.659
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m1
## m2
## m3
## m9 -1.1200
## m4
## m10 -1.0990 -2.245
## m11 -0.7085
## m12 -0.6872 -5.625
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik
## m0 3 -65.648
## m1 4 -64.401
## m2 4 -65.422
## m3 5 -64.387
## m9 6 -62.784
## m4 5 -65.112
## m10 8 -61.520
## m11 0.3918 8 -62.588
## m12 0.4581 -3.171 11 -59.386
## AICc delta weight
## m0 138.3 0.00 0.361
## m1 138.5 0.21 0.325
## m2 140.5 2.25 0.117
## m3 141.4 3.13 0.076
## m9 141.4 3.13 0.076
## m4 142.8 4.58 0.037
## m10 146.2 7.98 0.007
## m11 148.4 10.12 0.002
## m12 156.3 18.04 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 24.250 -2.361
## m1 16.320 -1.236 -0.63400
## m2 29.120 -3.068 0.5355
## m3 17.160 -1.348 -0.70270 -0.06662
## m9 16.050 -1.197 -0.63560 0.1607
## m4 36.520 -4.070 0.6074 -1.546
## m10 22.350 -2.028 0.09693 0.2321 -1.808
## m11 14.980 -1.031 -0.70650 -0.2313 -0.06561
## m12 -1.864 1.327 1.08400 -0.4493 0.97940 1.659
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m1
## m2
## m3
## m9 -1.1200
## m4
## m10 -1.0990 -2.245
## m11 -0.7085
## m12 -0.6872 -5.625
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik
## m0 3 -65.648
## m1 4 -64.401
## m2 4 -65.422
## m3 5 -64.387
## m9 6 -62.784
## m4 5 -65.112
## m10 8 -61.520
## m11 0.3918 8 -62.588
## m12 0.4581 -3.171 11 -59.386
## AICc delta weight
## m0 138.3 0.00 0.361
## m1 138.5 0.21 0.325
## m2 140.5 2.25 0.117
## m3 141.4 3.13 0.076
## m9 141.4 3.13 0.076
## m4 142.8 4.58 0.037
## m10 146.2 7.98 0.007
## m11 148.4 10.12 0.002
## m12 156.3 18.04 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 24.250 -2.361
## m1 16.320 -1.236 -0.63400
## m2 29.120 -3.068 0.5355
## m3 17.160 -1.348 -0.70270 -0.06662
## m9 16.050 -1.197 -0.63560 0.1607
## m4 36.520 -4.070 0.6074 -1.546
## m10 22.350 -2.028 0.09693 0.2321 -1.808
## m11 14.980 -1.031 -0.70650 -0.2313 -0.06561
## m12 -1.864 1.327 1.08400 -0.4493 0.97940 1.659
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m1
## m2
## m3
## m9 -1.1200
## m4
## m10 -1.0990 -2.245
## m11 -0.7085
## m12 -0.6872 -5.625
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik
## m0 3 -65.648
## m1 4 -64.401
## m2 4 -65.422
## m3 5 -64.387
## m9 6 -62.784
## m4 5 -65.112
## m10 8 -61.520
## m11 0.3918 8 -62.588
## m12 0.4581 -3.171 11 -59.386
## AICc delta weight
## m0 138.3 0.00 0.361
## m1 138.5 0.21 0.325
## m2 140.5 2.25 0.117
## m3 141.4 3.13 0.076
## m9 141.4 3.13 0.076
## m4 142.8 4.58 0.037
## m10 146.2 7.98 0.007
## m11 148.4 10.12 0.002
## m12 156.3 18.04 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 24.250 -2.361
## m1 16.320 -1.236 -0.63400
## m2 29.120 -3.068 0.5355
## m3 17.160 -1.348 -0.70270 -0.06662
## m9 16.050 -1.197 -0.63560 0.1607
## m4 36.520 -4.070 0.6074 -1.546
## m10 22.350 -2.028 0.09693 0.2321 -1.808
## m11 14.980 -1.031 -0.70650 -0.2313 -0.06561
## m12 -1.864 1.327 1.08400 -0.4493 0.97940 1.659
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m1
## m2
## m3
## m9 -1.1200
## m4
## m10 -1.0990 -2.245
## m11 -0.7085
## m12 -0.6872 -5.625
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik
## m0 3 -65.648
## m1 4 -64.401
## m2 4 -65.422
## m3 5 -64.387
## m9 6 -62.784
## m4 5 -65.112
## m10 8 -61.520
## m11 0.3918 8 -62.588
## m12 0.4581 -3.171 11 -59.386
## AICc delta weight
## m0 138.3 0.00 0.361
## m1 138.5 0.21 0.325
## m2 140.5 2.25 0.117
## m3 141.4 3.13 0.076
## m9 141.4 3.13 0.076
## m4 142.8 4.58 0.037
## m10 146.2 7.98 0.007
## m11 148.4 10.12 0.002
## m12 156.3 18.04 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m4 0.4425 0.3886 -0.4575 0.7848
## m2 1.5100 0.2379 -0.4837
## m0 -0.5452 0.6175
## m9 1.5330 0.2330 -0.01339 -0.4545
## m1 -0.6248 0.6310 -0.02638
## m10 -0.1745 0.4945 -0.14820 -0.4102 0.9057
## m3 -0.5159 0.6210 -0.08991 -0.05964
## m11 1.5510 0.2403 -0.08167 -0.5785 -0.06355
## m12 -0.2813 0.5316 -0.26780 -0.5354 -0.10750 0.7831
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m4
## m2
## m0
## m9 0.1230
## m1
## m10 0.1183 0.3943
## m3
## m11 0.2701
## m12 0.2721 0.5615
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik AICc
## m4 5 -21.879 56.3
## m2 4 -23.579 56.8
## m0 3 -26.678 60.3
## m9 6 -23.278 62.2
## m1 4 -26.645 62.9
## m10 8 -21.037 64.9
## m3 5 -26.468 65.4
## m11 0.1375 8 -22.636 68.1
## m12 0.1443 0.1466 11 -20.190 77.0
## delta weight
## m4 0.00 0.494
## m2 0.50 0.385
## m0 4.02 0.066
## m9 5.95 0.025
## m1 6.63 0.018
## m10 8.67 0.006
## m3 9.18 0.005
## m11 11.87 0.001
## m12 20.79 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m4 0.4425 0.3886 -0.4575 0.7848
## m2 1.5100 0.2379 -0.4837
## m0 -0.5452 0.6175
## m9 1.5330 0.2330 -0.01339 -0.4545
## m1 -0.6248 0.6310 -0.02638
## m10 -0.1745 0.4945 -0.14820 -0.4102 0.9057
## m3 -0.5159 0.6210 -0.08991 -0.05964
## m11 1.5510 0.2403 -0.08167 -0.5785 -0.06355
## m12 -0.2813 0.5316 -0.26780 -0.5354 -0.10750 0.7831
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m4
## m2
## m0
## m9 0.1230
## m1
## m10 0.1183 0.3943
## m3
## m11 0.2701
## m12 0.2721 0.5615
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik AICc
## m4 5 -21.879 56.3
## m2 4 -23.579 56.8
## m0 3 -26.678 60.3
## m9 6 -23.278 62.2
## m1 4 -26.645 62.9
## m10 8 -21.037 64.9
## m3 5 -26.468 65.4
## m11 0.1375 8 -22.636 68.1
## m12 0.1443 0.1466 11 -20.190 77.0
## delta weight
## m4 0.00 0.494
## m2 0.50 0.385
## m0 4.02 0.066
## m9 5.95 0.025
## m1 6.63 0.018
## m10 8.67 0.006
## m3 9.18 0.005
## m11 11.87 0.001
## m12 20.79 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m4 0.4425 0.3886 -0.4575 0.7848
## m2 1.5100 0.2379 -0.4837
## m0 -0.5452 0.6175
## m9 1.5330 0.2330 -0.01339 -0.4545
## m1 -0.6248 0.6310 -0.02638
## m10 -0.1745 0.4945 -0.14820 -0.4102 0.9057
## m3 -0.5159 0.6210 -0.08991 -0.05964
## m11 1.5510 0.2403 -0.08167 -0.5785 -0.06355
## m12 -0.2813 0.5316 -0.26780 -0.5354 -0.10750 0.7831
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m4
## m2
## m0
## m9 0.1230
## m1
## m10 0.1183 0.3943
## m3
## m11 0.2701
## m12 0.2721 0.5615
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik AICc
## m4 5 -21.879 56.3
## m2 4 -23.579 56.8
## m0 3 -26.678 60.3
## m9 6 -23.278 62.2
## m1 4 -26.645 62.9
## m10 8 -21.037 64.9
## m3 5 -26.468 65.4
## m11 0.1375 8 -22.636 68.1
## m12 0.1443 0.1466 11 -20.190 77.0
## delta weight
## m4 0.00 0.494
## m2 0.50 0.385
## m0 4.02 0.066
## m9 5.95 0.025
## m1 6.63 0.018
## m10 8.67 0.006
## m3 9.18 0.005
## m11 11.87 0.001
## m12 20.79 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 -1.191 0.8554
## m2 -1.090 0.8357 -0.3568
## m1 -1.536 0.9285 0.1323
## m4 -1.025 0.8870 -0.3565 -1.026
## m3 -1.725 0.9500 0.2256 0.08678
## m9 -3.722 1.3530 0.1402 -0.2694
## m10 -5.521 1.7850 0.5080 -0.2523 -1.333
## m11 -3.914 1.3740 0.2372 -0.2742 0.09018
## m12 -6.050 1.8800 0.5614 -0.2193 0.04327 -1.486
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m2
## m1
## m4
## m3
## m9 0.3431
## m10 0.4015 -1.1230
## m11 0.3527
## m12 0.3800 -0.9693
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik
## m0 3 -41.784
## m2 4 -41.126
## m1 4 -41.476
## m4 5 -40.209
## m3 5 -41.336
## m9 6 -40.209
## m10 8 -37.863
## m11 0.006226 8 -40.045
## m12 -0.032100 0.1592 11 -37.618
## AICc delta weight
## m0 90.5 0.00 0.428
## m2 91.9 1.36 0.217
## m1 92.6 2.06 0.153
## m4 92.9 2.43 0.127
## m3 95.2 4.68 0.041
## m9 96.1 5.58 0.026
## m10 98.6 8.09 0.007
## m11 102.9 12.45 0.001
## m12 111.9 21.41 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 -1.191 0.8554
## m2 -1.090 0.8357 -0.3568
## m1 -1.536 0.9285 0.1323
## m4 -1.025 0.8870 -0.3565 -1.026
## m3 -1.725 0.9500 0.2256 0.08678
## m9 -3.722 1.3530 0.1402 -0.2694
## m10 -5.521 1.7850 0.5080 -0.2523 -1.333
## m11 -3.914 1.3740 0.2372 -0.2742 0.09018
## m12 -6.050 1.8800 0.5614 -0.2193 0.04327 -1.486
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m2
## m1
## m4
## m3
## m9 0.3431
## m10 0.4015 -1.1230
## m11 0.3527
## m12 0.3800 -0.9693
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik
## m0 3 -41.784
## m2 4 -41.126
## m1 4 -41.476
## m4 5 -40.209
## m3 5 -41.336
## m9 6 -40.209
## m10 8 -37.863
## m11 0.006226 8 -40.045
## m12 -0.032100 0.1592 11 -37.618
## AICc delta weight
## m0 90.5 0.00 0.428
## m2 91.9 1.36 0.217
## m1 92.6 2.06 0.153
## m4 92.9 2.43 0.127
## m3 95.2 4.68 0.041
## m9 96.1 5.58 0.026
## m10 98.6 8.09 0.007
## m11 102.9 12.45 0.001
## m12 111.9 21.41 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m4 6.070 -0.6758 -0.7128 2.158
## m10 6.092 -0.6916 -0.2620 -0.7406 2.234
## m2 4.696 -0.3203 -0.5503
## m12 5.870 -0.6697 -0.1429 -0.6885 0.11210 2.433
## m0 3.576 -0.1276
## m9 4.774 -0.3410 -0.1717 -0.5822
## m1 3.642 -0.1458 -0.1635
## m3 3.630 -0.1472 -0.1398 0.02218
## m11 4.710 -0.3342 -0.1432 -0.5055 0.02638
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m4
## m10 -0.08402 0.2360
## m2
## m12 -0.13410 -0.0196
## m0
## m9 -0.09193
## m1
## m3
## m11 -0.17860
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik AICc
## m4 5 -20.143 52.8
## m10 8 -16.842 56.5
## m2 4 -29.510 68.6
## m12 -0.04635 -0.244 11 -16.377 69.4
## m0 3 -32.176 71.3
## m9 6 -28.174 72.0
## m1 4 -31.260 72.1
## m3 5 -31.242 75.0
## m11 -0.08081 8 -28.049 79.0
## delta weight
## m4 0.00 0.867
## m10 3.75 0.133
## m2 15.83 0.000
## m12 16.64 0.000
## m0 18.49 0.000
## m9 19.21 0.000
## m1 19.33 0.000
## m3 22.20 0.000
## m11 26.17 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m4 7.462 -0.8890 0.008263
## m2 7.087 -0.8187 1.338
## m5 5.932 -0.5937 0.0135
## m6 6.992 -0.7508
## m3 6.554 -0.6890 0.121
## m1 6.583 0.0004831 -0.6952
## lst_wet df logLik AICc delta weight
## m4 4 -35.740 81.1 0.00 0.200
## m2 4 -35.760 81.1 0.04 0.196
## m5 4 -35.774 81.1 0.07 0.193
## m6 -0.004044 4 -36.081 81.8 0.68 0.142
## m3 4 -36.139 81.9 0.80 0.134
## m1 4 -36.143 81.9 0.81 0.134
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m4 7.462 -0.8890 0.008263
## m2 7.087 -0.8187 1.338
## m5 5.932 -0.5937 0.0135
## m6 6.992 -0.7508
## m3 6.554 -0.6890 0.121
## m1 6.583 0.0004831 -0.6952
## lst_wet df logLik AICc delta weight
## m4 4 -35.740 81.1 0.00 0.200
## m2 4 -35.760 81.1 0.04 0.196
## m5 4 -35.774 81.1 0.07 0.193
## m6 -0.004044 4 -36.081 81.8 0.68 0.142
## m3 4 -36.139 81.9 0.80 0.134
## m1 4 -36.143 81.9 0.81 0.134
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m4 7.462 -0.8890 0.008263
## m2 7.087 -0.8187 1.338
## m5 5.932 -0.5937 0.0135
## m6 6.992 -0.7508
## m3 6.554 -0.6890 0.121
## m1 6.583 0.0004831 -0.6952
## lst_wet df logLik AICc delta weight
## m4 4 -35.740 81.1 0.00 0.200
## m2 4 -35.760 81.1 0.04 0.196
## m5 4 -35.774 81.1 0.07 0.193
## m6 -0.004044 4 -36.081 81.8 0.68 0.142
## m3 4 -36.139 81.9 0.80 0.134
## m1 4 -36.143 81.9 0.81 0.134
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m4 7.462 -0.8890 0.008263
## m2 7.087 -0.8187 1.338
## m5 5.932 -0.5937 0.0135
## m6 6.992 -0.7508
## m3 6.554 -0.6890 0.121
## m1 6.583 0.0004831 -0.6952
## lst_wet df logLik AICc delta weight
## m4 4 -35.740 81.1 0.00 0.200
## m2 4 -35.760 81.1 0.04 0.196
## m5 4 -35.774 81.1 0.07 0.193
## m6 -0.004044 4 -36.081 81.8 0.68 0.142
## m3 4 -36.139 81.9 0.80 0.134
## m1 4 -36.143 81.9 0.81 0.134
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m4 7.462 -0.8890 0.008263
## m2 7.087 -0.8187 1.338
## m5 5.932 -0.5937 0.0135
## m6 6.992 -0.7508
## m3 6.554 -0.6890 0.121
## m1 6.583 0.0004831 -0.6952
## lst_wet df logLik AICc delta weight
## m4 4 -35.740 81.1 0.00 0.200
## m2 4 -35.760 81.1 0.04 0.196
## m5 4 -35.774 81.1 0.07 0.193
## m6 -0.004044 4 -36.081 81.8 0.68 0.142
## m3 4 -36.139 81.9 0.80 0.134
## m1 4 -36.143 81.9 0.81 0.134
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m2 22.80 -2.017 -14.56
## m6 26.25 -2.759
## m4 24.96 -2.289 -0.01885
## m5 23.03 -2.167 -0.06285
## m3 23.50 -2.241 -1.529
## m1 24.04 -0.001138 -2.325
## lst_wet df logLik AICc delta weight
## m2 4 -64.672 139.0 0.00 0.273
## m6 0.0246 4 -64.944 139.6 0.54 0.208
## m4 4 -65.245 140.2 1.15 0.154
## m5 4 -65.312 140.3 1.28 0.144
## m3 4 -65.516 140.7 1.69 0.117
## m1 4 -65.644 141.0 1.94 0.103
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m2 22.80 -2.017 -14.56
## m6 26.25 -2.759
## m4 24.96 -2.289 -0.01885
## m5 23.03 -2.167 -0.06285
## m3 23.50 -2.241 -1.529
## m1 24.04 -0.001138 -2.325
## lst_wet df logLik AICc delta weight
## m2 4 -64.672 139.0 0.00 0.273
## m6 0.0246 4 -64.944 139.6 0.54 0.208
## m4 4 -65.245 140.2 1.15 0.154
## m5 4 -65.312 140.3 1.28 0.144
## m3 4 -65.516 140.7 1.69 0.117
## m1 4 -65.644 141.0 1.94 0.103
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m2 22.80 -2.017 -14.56
## m6 26.25 -2.759
## m4 24.96 -2.289 -0.01885
## m5 23.03 -2.167 -0.06285
## m3 23.50 -2.241 -1.529
## m1 24.04 -0.001138 -2.325
## lst_wet df logLik AICc delta weight
## m2 4 -64.672 139.0 0.00 0.273
## m6 0.0246 4 -64.944 139.6 0.54 0.208
## m4 4 -65.245 140.2 1.15 0.154
## m5 4 -65.312 140.3 1.28 0.144
## m3 4 -65.516 140.7 1.69 0.117
## m1 4 -65.644 141.0 1.94 0.103
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m2 22.80 -2.017 -14.56
## m6 26.25 -2.759
## m4 24.96 -2.289 -0.01885
## m5 23.03 -2.167 -0.06285
## m3 23.50 -2.241 -1.529
## m1 24.04 -0.001138 -2.325
## lst_wet df logLik AICc delta weight
## m2 4 -64.672 139.0 0.00 0.273
## m6 0.0246 4 -64.944 139.6 0.54 0.208
## m4 4 -65.245 140.2 1.15 0.154
## m5 4 -65.312 140.3 1.28 0.144
## m3 4 -65.516 140.7 1.69 0.117
## m1 4 -65.644 141.0 1.94 0.103
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m4 -2.33600 1.0830 -0.01485
## m5 -0.05599 0.5342 -0.03632
## m3 -0.18380 0.5552 -1.148
## m6 -0.49570 0.6094
## m2 -0.53920 0.6159 0.01239
## m1 -0.52720 -9.991e-05 0.6147
## lst_wet df logLik AICc delta weight
## m4 4 -25.517 60.6 0.00 0.369
## m5 4 -26.392 62.4 1.75 0.154
## m3 4 -26.559 62.7 2.08 0.130
## m6 -0.001172 4 -26.675 63.0 2.32 0.116
## m2 4 -26.678 63.0 2.32 0.116
## m1 4 -26.678 63.0 2.32 0.116
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m4 -2.33600 1.0830 -0.01485
## m5 -0.05599 0.5342 -0.03632
## m3 -0.18380 0.5552 -1.148
## m6 -0.49570 0.6094
## m2 -0.53920 0.6159 0.01239
## m1 -0.52720 -9.991e-05 0.6147
## lst_wet df logLik AICc delta weight
## m4 4 -25.517 60.6 0.00 0.369
## m5 4 -26.392 62.4 1.75 0.154
## m3 4 -26.559 62.7 2.08 0.130
## m6 -0.001172 4 -26.675 63.0 2.32 0.116
## m2 4 -26.678 63.0 2.32 0.116
## m1 4 -26.678 63.0 2.32 0.116
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m4 -2.33600 1.0830 -0.01485
## m5 -0.05599 0.5342 -0.03632
## m3 -0.18380 0.5552 -1.148
## m6 -0.49570 0.6094
## m2 -0.53920 0.6159 0.01239
## m1 -0.52720 -9.991e-05 0.6147
## lst_wet df logLik AICc delta weight
## m4 4 -25.517 60.6 0.00 0.369
## m5 4 -26.392 62.4 1.75 0.154
## m3 4 -26.559 62.7 2.08 0.130
## m6 -0.001172 4 -26.675 63.0 2.32 0.116
## m2 4 -26.678 63.0 2.32 0.116
## m1 4 -26.678 63.0 2.32 0.116
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry lst_wet
## m5 -6.329 1.7390 0.3826
## m4 -6.122 2.0260 -0.0581
## m3 -7.266 1.9390 2.281
## m1 -7.232 0.01824 1.8120
## m6 -1.516 0.8680 0.01669
## m2 -1.211 0.8568 0.06657
## df logLik AICc delta weight
## m5 4 -37.355 84.3 0.00 0.375
## m4 4 -37.685 85.0 0.66 0.270
## m3 4 -38.132 85.9 1.55 0.173
## m1 4 -38.188 86.0 1.66 0.163
## m6 4 -40.600 90.8 6.49 0.015
## m2 4 -41.783 93.2 8.86 0.004
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry lst_wet
## m5 -6.329 1.7390 0.3826
## m4 -6.122 2.0260 -0.0581
## m3 -7.266 1.9390 2.281
## m1 -7.232 0.01824 1.8120
## m6 -1.516 0.8680 0.01669
## m2 -1.211 0.8568 0.06657
## df logLik AICc delta weight
## m5 4 -37.355 84.3 0.00 0.375
## m4 4 -37.685 85.0 0.66 0.270
## m3 4 -38.132 85.9 1.55 0.173
## m1 4 -38.188 86.0 1.66 0.163
## m6 4 -40.600 90.8 6.49 0.015
## m2 4 -41.783 93.2 8.86 0.004
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m1 3.047 0.005279 -0.06742
## m4 3.457 -0.07427 -0.003794
## m2 3.608 -0.11670 -1.002
## m5 3.446 -0.10750 0.03864
## m6 3.548 -0.12750
## m3 3.623 -0.13470 -0.2559
## lst_wet df logLik AICc delta weight
## m1 4 -31.991 73.6 0.00 0.187
## m4 4 -32.095 73.8 0.21 0.169
## m2 4 -32.099 73.8 0.22 0.168
## m5 4 -32.139 73.9 0.30 0.161
## m6 0.001693 4 -32.154 73.9 0.33 0.159
## m3 4 -32.170 73.9 0.36 0.156
## Models ranked by AICc(x)
## Error in nrow(concord.out): object 'concord.out' not found
concord.out9b<-concord.magic(sites, "scaled.n15.bromeliad.final", no126data, 10, 2, "gaussian")
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m1 1.417 -0.026640 0.02762
## m0 1.571 -0.054190
## m3 1.324 -0.012770 0.04290 0.01360
## m2 1.530 -0.047110 0.02392
## m10 1.083 0.034730 0.08012 0.02240 -0.07387
## m9 1.343 -0.013950 0.02827 0.02021
## m4 1.525 -0.044800 0.02404 -0.02925
## m11 1.255 -0.001098 0.04410 0.01451 0.01417
## m12 0.760 0.084370 0.12890 0.02150 0.03909 -0.02230
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m1
## m0
## m3
## m2
## m10 -0.02385 -0.1541
## m9 -0.02230
## m4
## m11 -0.01533
## m12 -0.02175 -0.2373
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik AICc
## m1 4 33.979 -58.4
## m0 3 32.111 -57.3
## m3 5 34.487 -56.5
## m2 4 32.509 -55.4
## m10 8 38.825 -54.8
## m9 6 34.947 -54.2
## m4 5 32.607 -52.7
## m11 0.006887 8 35.590 -48.3
## m12 0.003435 -0.06497 11 40.702 -44.7
## delta weight
## m1 0.00 0.389
## m0 1.06 0.229
## m3 1.89 0.151
## m2 2.94 0.089
## m10 3.57 0.065
## m9 4.12 0.050
## m4 5.64 0.023
## m11 10.04 0.003
## m12 13.62 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m1 1.417 -0.026640 0.02762
## m0 1.571 -0.054190
## m3 1.324 -0.012770 0.04290 0.01360
## m2 1.530 -0.047110 0.02392
## m10 1.083 0.034730 0.08012 0.02240 -0.07387
## m9 1.343 -0.013950 0.02827 0.02021
## m4 1.525 -0.044800 0.02404 -0.02925
## m11 1.255 -0.001098 0.04410 0.01451 0.01417
## m12 0.760 0.084370 0.12890 0.02150 0.03909 -0.02230
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m1
## m0
## m3
## m2
## m10 -0.02385 -0.1541
## m9 -0.02230
## m4
## m11 -0.01533
## m12 -0.02175 -0.2373
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik AICc
## m1 4 33.979 -58.4
## m0 3 32.111 -57.3
## m3 5 34.487 -56.5
## m2 4 32.509 -55.4
## m10 8 38.825 -54.8
## m9 6 34.947 -54.2
## m4 5 32.607 -52.7
## m11 0.006887 8 35.590 -48.3
## m12 0.003435 -0.06497 11 40.702 -44.7
## delta weight
## m1 0.00 0.389
## m0 1.06 0.229
## m3 1.89 0.151
## m2 2.94 0.089
## m10 3.57 0.065
## m9 4.12 0.050
## m4 5.64 0.023
## m11 10.04 0.003
## m12 13.62 0.000
## Models ranked by AICc(x)
## Warning in matrix(value, n, p): data length [11] is not a sub-multiple or
## multiple of the number of columns [10]
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m1 1.417 -0.026640 0.02762
## m0 1.571 -0.054190
## m3 1.324 -0.012770 0.04290 0.01360
## m2 1.530 -0.047110 0.02392
## m10 1.083 0.034730 0.08012 0.02240 -0.07387
## m9 1.343 -0.013950 0.02827 0.02021
## m4 1.525 -0.044800 0.02404 -0.02925
## m11 1.255 -0.001098 0.04410 0.01451 0.01417
## m12 0.760 0.084370 0.12890 0.02150 0.03909 -0.02230
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m1
## m0
## m3
## m2
## m10 -0.02385 -0.1541
## m9 -0.02230
## m4
## m11 -0.01533
## m12 -0.02175 -0.2373
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik AICc
## m1 4 33.979 -58.4
## m0 3 32.111 -57.3
## m3 5 34.487 -56.5
## m2 4 32.509 -55.4
## m10 8 38.825 -54.8
## m9 6 34.947 -54.2
## m4 5 32.607 -52.7
## m11 0.006887 8 35.590 -48.3
## m12 0.003435 -0.06497 11 40.702 -44.7
## delta weight
## m1 0.00 0.389
## m0 1.06 0.229
## m3 1.89 0.151
## m2 2.94 0.089
## m10 3.57 0.065
## m9 4.12 0.050
## m4 5.64 0.023
## m11 10.04 0.003
## m12 13.62 0.000
## Models ranked by AICc(x)
## Warning in matrix(value, n, p): data length [11] is not a sub-multiple or
## multiple of the number of columns [10]
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m1 1.417 -0.026640 0.02762
## m0 1.571 -0.054190
## m3 1.324 -0.012770 0.04290 0.01360
## m2 1.530 -0.047110 0.02392
## m10 1.083 0.034730 0.08012 0.02240 -0.07387
## m9 1.343 -0.013950 0.02827 0.02021
## m4 1.525 -0.044800 0.02404 -0.02925
## m11 1.255 -0.001098 0.04410 0.01451 0.01417
## m12 0.760 0.084370 0.12890 0.02150 0.03909 -0.02230
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m1
## m0
## m3
## m2
## m10 -0.02385 -0.1541
## m9 -0.02230
## m4
## m11 -0.01533
## m12 -0.02175 -0.2373
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik AICc
## m1 4 33.979 -58.4
## m0 3 32.111 -57.3
## m3 5 34.487 -56.5
## m2 4 32.509 -55.4
## m10 8 38.825 -54.8
## m9 6 34.947 -54.2
## m4 5 32.607 -52.7
## m11 0.006887 8 35.590 -48.3
## m12 0.003435 -0.06497 11 40.702 -44.7
## delta weight
## m1 0.00 0.389
## m0 1.06 0.229
## m3 1.89 0.151
## m2 2.94 0.089
## m10 3.57 0.065
## m9 4.12 0.050
## m4 5.64 0.023
## m11 10.04 0.003
## m12 13.62 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m1 1.417 -0.026640 0.02762
## m0 1.571 -0.054190
## m3 1.324 -0.012770 0.04290 0.01360
## m2 1.530 -0.047110 0.02392
## m10 1.083 0.034730 0.08012 0.02240 -0.07387
## m9 1.343 -0.013950 0.02827 0.02021
## m4 1.525 -0.044800 0.02404 -0.02925
## m11 1.255 -0.001098 0.04410 0.01451 0.01417
## m12 0.760 0.084370 0.12890 0.02150 0.03909 -0.02230
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m1
## m0
## m3
## m2
## m10 -0.02385 -0.1541
## m9 -0.02230
## m4
## m11 -0.01533
## m12 -0.02175 -0.2373
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik AICc
## m1 4 33.979 -58.4
## m0 3 32.111 -57.3
## m3 5 34.487 -56.5
## m2 4 32.509 -55.4
## m10 8 38.825 -54.8
## m9 6 34.947 -54.2
## m4 5 32.607 -52.7
## m11 0.006887 8 35.590 -48.3
## m12 0.003435 -0.06497 11 40.702 -44.7
## delta weight
## m1 0.00 0.389
## m0 1.06 0.229
## m3 1.89 0.151
## m2 2.94 0.089
## m10 3.57 0.065
## m9 4.12 0.050
## m4 5.64 0.023
## m11 10.04 0.003
## m12 13.62 0.000
## Models ranked by AICc(x)
## Warning in matrix(value, n, p): data length [11] is not a sub-multiple or
## multiple of the number of columns [10]
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m1 2.198 -0.07838 -0.0351200
## m0 2.637 -0.14070
## m2 2.828 -0.16840 0.020980
## m3 2.311 -0.09360 -0.0444900 -0.009074
## m9 2.114 -0.06620 -0.0353600 0.001038
## m4 3.231 -0.22300 0.024910 -0.08432
## m10 2.457 -0.11160 0.0005586 0.004896 -0.09551
## m11 2.147 -0.06978 -0.0447400 -0.015390 -0.008959
## m12 1.233 0.05815 0.0491000 -0.027270 0.048230 0.09253
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m1
## m0
## m2
## m3
## m9 -0.05916
## m4
## m10 -0.05803 -0.1099
## m11 -0.04169
## m12 -0.04052 -0.2948
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik AICc
## m1 4 21.159 -32.7
## m0 3 19.768 -32.6
## m2 4 19.893 -30.1
## m3 5 21.258 -29.9
## m9 6 22.697 -29.6
## m4 5 20.225 -27.8
## m10 8 23.845 -24.5
## m11 0.01655 8 22.919 -22.6
## m12 0.02017 -0.1736 11 26.193 -14.9
## delta weight
## m1 0.00 0.354
## m0 0.08 0.341
## m2 2.53 0.100
## m3 2.74 0.090
## m9 3.08 0.076
## m4 4.81 0.032
## m10 8.16 0.006
## m11 10.01 0.002
## m12 17.79 0.000
## Models ranked by AICc(x)
## Warning in matrix(value, n, p): data length [11] is not a sub-multiple or
## multiple of the number of columns [10]
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m1 2.198 -0.07838 -0.0351200
## m0 2.637 -0.14070
## m2 2.828 -0.16840 0.020980
## m3 2.311 -0.09360 -0.0444900 -0.009074
## m9 2.114 -0.06620 -0.0353600 0.001038
## m4 3.231 -0.22300 0.024910 -0.08432
## m10 2.457 -0.11160 0.0005586 0.004896 -0.09551
## m11 2.147 -0.06978 -0.0447400 -0.015390 -0.008959
## m12 1.233 0.05815 0.0491000 -0.027270 0.048230 0.09253
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m1
## m0
## m2
## m3
## m9 -0.05916
## m4
## m10 -0.05803 -0.1099
## m11 -0.04169
## m12 -0.04052 -0.2948
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik AICc
## m1 4 21.159 -32.7
## m0 3 19.768 -32.6
## m2 4 19.893 -30.1
## m3 5 21.258 -29.9
## m9 6 22.697 -29.6
## m4 5 20.225 -27.8
## m10 8 23.845 -24.5
## m11 0.01655 8 22.919 -22.6
## m12 0.02017 -0.1736 11 26.193 -14.9
## delta weight
## m1 0.00 0.354
## m0 0.08 0.341
## m2 2.53 0.100
## m3 2.74 0.090
## m9 3.08 0.076
## m4 4.81 0.032
## m10 8.16 0.006
## m11 10.01 0.002
## m12 17.79 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m1 2.198 -0.07838 -0.0351200
## m0 2.637 -0.14070
## m2 2.828 -0.16840 0.020980
## m3 2.311 -0.09360 -0.0444900 -0.009074
## m9 2.114 -0.06620 -0.0353600 0.001038
## m4 3.231 -0.22300 0.024910 -0.08432
## m10 2.457 -0.11160 0.0005586 0.004896 -0.09551
## m11 2.147 -0.06978 -0.0447400 -0.015390 -0.008959
## m12 1.233 0.05815 0.0491000 -0.027270 0.048230 0.09253
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m1
## m0
## m2
## m3
## m9 -0.05916
## m4
## m10 -0.05803 -0.1099
## m11 -0.04169
## m12 -0.04052 -0.2948
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik AICc
## m1 4 21.159 -32.7
## m0 3 19.768 -32.6
## m2 4 19.893 -30.1
## m3 5 21.258 -29.9
## m9 6 22.697 -29.6
## m4 5 20.225 -27.8
## m10 8 23.845 -24.5
## m11 0.01655 8 22.919 -22.6
## m12 0.02017 -0.1736 11 26.193 -14.9
## delta weight
## m1 0.00 0.354
## m0 0.08 0.341
## m2 2.53 0.100
## m3 2.74 0.090
## m9 3.08 0.076
## m4 4.81 0.032
## m10 8.16 0.006
## m11 10.01 0.002
## m12 17.79 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m1 2.198 -0.07838 -0.0351200
## m0 2.637 -0.14070
## m2 2.828 -0.16840 0.020980
## m3 2.311 -0.09360 -0.0444900 -0.009074
## m9 2.114 -0.06620 -0.0353600 0.001038
## m4 3.231 -0.22300 0.024910 -0.08432
## m10 2.457 -0.11160 0.0005586 0.004896 -0.09551
## m11 2.147 -0.06978 -0.0447400 -0.015390 -0.008959
## m12 1.233 0.05815 0.0491000 -0.027270 0.048230 0.09253
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m1
## m0
## m2
## m3
## m9 -0.05916
## m4
## m10 -0.05803 -0.1099
## m11 -0.04169
## m12 -0.04052 -0.2948
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik AICc
## m1 4 21.159 -32.7
## m0 3 19.768 -32.6
## m2 4 19.893 -30.1
## m3 5 21.258 -29.9
## m9 6 22.697 -29.6
## m4 5 20.225 -27.8
## m10 8 23.845 -24.5
## m11 0.01655 8 22.919 -22.6
## m12 0.02017 -0.1736 11 26.193 -14.9
## delta weight
## m1 0.00 0.354
## m0 0.08 0.341
## m2 2.53 0.100
## m3 2.74 0.090
## m9 3.08 0.076
## m4 4.81 0.032
## m10 8.16 0.006
## m11 10.01 0.002
## m12 17.79 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m4 1.0940 0.03137 -0.05003 0.07771
## m2 1.2000 0.01645 -0.05262
## m0 0.9760 0.05774
## m9 1.2050 0.01538 -0.0007095 -0.04932
## m1 0.9696 0.05883 -0.0021280
## m10 1.0260 0.04308 -0.0162900 -0.04462 0.09162
## m3 0.9809 0.05779 -0.0087130 -0.006182
## m11 1.2070 0.01620 -0.0078130 -0.06261 -0.006609
## m12 1.0110 0.04770 -0.0300600 -0.05794 -0.012340 0.07576
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m4
## m2
## m0
## m9 0.01426
## m1
## m10 0.01376 0.04581
## m3
## m11 0.03003
## m12 0.03025 0.06732
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik AICc
## m4 5 47.102 -81.7
## m2 4 45.444 -81.3
## m0 3 41.851 -76.8
## m9 6 45.836 -76.0
## m1 4 41.872 -74.1
## m10 8 48.222 -73.6
## m3 5 42.056 -71.6
## m11 0.01474 8 46.561 -70.3
## m12 0.01548 0.01891 11 49.234 -61.8
## delta weight
## m4 0.00 0.499
## m2 0.42 0.405
## m0 4.92 0.042
## m9 5.68 0.029
## m1 7.56 0.011
## m10 8.12 0.009
## m3 10.09 0.003
## m11 11.44 0.002
## m12 19.90 0.000
## Models ranked by AICc(x)
## Warning in matrix(value, n, p): data length [11] is not a sub-multiple or
## multiple of the number of columns [10]
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m4 1.0940 0.03137 -0.05003 0.07771
## m2 1.2000 0.01645 -0.05262
## m0 0.9760 0.05774
## m9 1.2050 0.01538 -0.0007095 -0.04932
## m1 0.9696 0.05883 -0.0021280
## m10 1.0260 0.04308 -0.0162900 -0.04462 0.09162
## m3 0.9809 0.05779 -0.0087130 -0.006182
## m11 1.2070 0.01620 -0.0078130 -0.06261 -0.006609
## m12 1.0110 0.04770 -0.0300600 -0.05794 -0.012340 0.07576
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m4
## m2
## m0
## m9 0.01426
## m1
## m10 0.01376 0.04581
## m3
## m11 0.03003
## m12 0.03025 0.06732
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik AICc
## m4 5 47.102 -81.7
## m2 4 45.444 -81.3
## m0 3 41.851 -76.8
## m9 6 45.836 -76.0
## m1 4 41.872 -74.1
## m10 8 48.222 -73.6
## m3 5 42.056 -71.6
## m11 0.01474 8 46.561 -70.3
## m12 0.01548 0.01891 11 49.234 -61.8
## delta weight
## m4 0.00 0.499
## m2 0.42 0.405
## m0 4.92 0.042
## m9 5.68 0.029
## m1 7.56 0.011
## m10 8.12 0.009
## m3 10.09 0.003
## m11 11.44 0.002
## m12 19.90 0.000
## Models ranked by AICc(x)
## Warning in matrix(value, n, p): data length [11] is not a sub-multiple or
## multiple of the number of columns [10]
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m4 1.0940 0.03137 -0.05003 0.07771
## m2 1.2000 0.01645 -0.05262
## m0 0.9760 0.05774
## m9 1.2050 0.01538 -0.0007095 -0.04932
## m1 0.9696 0.05883 -0.0021280
## m10 1.0260 0.04308 -0.0162900 -0.04462 0.09162
## m3 0.9809 0.05779 -0.0087130 -0.006182
## m11 1.2070 0.01620 -0.0078130 -0.06261 -0.006609
## m12 1.0110 0.04770 -0.0300600 -0.05794 -0.012340 0.07576
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m4
## m2
## m0
## m9 0.01426
## m1
## m10 0.01376 0.04581
## m3
## m11 0.03003
## m12 0.03025 0.06732
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik AICc
## m4 5 47.102 -81.7
## m2 4 45.444 -81.3
## m0 3 41.851 -76.8
## m9 6 45.836 -76.0
## m1 4 41.872 -74.1
## m10 8 48.222 -73.6
## m3 5 42.056 -71.6
## m11 0.01474 8 46.561 -70.3
## m12 0.01548 0.01891 11 49.234 -61.8
## delta weight
## m4 0.00 0.499
## m2 0.42 0.405
## m0 4.92 0.042
## m9 5.68 0.029
## m1 7.56 0.011
## m10 8.12 0.009
## m3 10.09 0.003
## m11 11.44 0.002
## m12 19.90 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 0.8679 0.08931
## m2 0.8810 0.08678 -0.04595
## m4 0.8868 0.09148 -0.04592 -0.09389
## m1 0.8404 0.09516 0.01057
## m9 0.5935 0.14310 0.01146 -0.03604
## m3 0.8198 0.09749 0.02069 0.0094140
## m10 0.4490 0.17850 0.04102 -0.03464 -0.11920
## m11 0.5937 0.14130 0.02189 -0.04083 0.0097660
## m12 0.4044 0.18730 0.04115 -0.03584 -0.0004945 -0.15010
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m2
## m4
## m1
## m9 0.03900
## m3
## m10 0.04379 -0.09020
## m11 0.04431
## m12 0.04680 -0.05621
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik AICc
## m0 3 27.351 -47.8
## m2 4 28.464 -47.3
## m4 5 29.255 -46.0
## m1 4 27.547 -45.5
## m9 6 29.470 -43.3
## m3 5 27.711 -42.9
## m10 8 31.231 -39.6
## m11 0.005184 8 29.689 -36.5
## m12 0.001701 0.03319 11 31.609 -26.6
## delta weight
## m0 0.00 0.364
## m2 0.45 0.291
## m4 1.77 0.151
## m1 2.28 0.116
## m9 4.49 0.039
## m3 4.86 0.032
## m10 8.17 0.006
## m11 11.26 0.001
## m12 21.23 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 0.8679 0.08931
## m2 0.8810 0.08678 -0.04595
## m4 0.8868 0.09148 -0.04592 -0.09389
## m1 0.8404 0.09516 0.01057
## m9 0.5935 0.14310 0.01146 -0.03604
## m3 0.8198 0.09749 0.02069 0.0094140
## m10 0.4490 0.17850 0.04102 -0.03464 -0.11920
## m11 0.5937 0.14130 0.02189 -0.04083 0.0097660
## m12 0.4044 0.18730 0.04115 -0.03584 -0.0004945 -0.15010
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m2
## m4
## m1
## m9 0.03900
## m3
## m10 0.04379 -0.09020
## m11 0.04431
## m12 0.04680 -0.05621
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik AICc
## m0 3 27.351 -47.8
## m2 4 28.464 -47.3
## m4 5 29.255 -46.0
## m1 4 27.547 -45.5
## m9 6 29.470 -43.3
## m3 5 27.711 -42.9
## m10 8 31.231 -39.6
## m11 0.005184 8 29.689 -36.5
## m12 0.001701 0.03319 11 31.609 -26.6
## delta weight
## m0 0.00 0.364
## m2 0.45 0.291
## m4 1.77 0.151
## m1 2.28 0.116
## m9 4.49 0.039
## m3 4.86 0.032
## m10 8.17 0.006
## m11 11.26 0.001
## m12 21.23 0.000
## Models ranked by AICc(x)
## Warning in matrix(value, n, p): data length [11] is not a sub-multiple or
## multiple of the number of columns [10]
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m4 1.776 -0.10210 -0.09235 0.3233
## m10 1.772 -0.10360 -0.04786 -0.09568 0.3391
## m2 1.570 -0.04889 -0.06799
## m0 1.431 -0.02508
## m1 1.442 -0.02799 -0.02614
## m9 1.582 -0.05222 -0.02715 -0.07246
## m12 1.761 -0.10260 -0.04167 -0.09186 0.005819 0.3467
## m3 1.441 -0.02806 -0.02495 0.001112
## m11 1.576 -0.05137 -0.02536 -0.06390 0.001635
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m4
## m10 -0.01088 0.05794
## m2
## m0
## m1
## m9 -0.01204
## m12 -0.01487 0.04806
## m3
## m11 -0.02167
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik AICc
## m4 5 34.098 -55.7
## m10 8 37.394 -51.9
## m2 4 25.931 -42.3
## m0 3 24.235 -41.5
## m1 4 25.244 -40.9
## m9 6 27.240 -38.8
## m12 -0.003704 -0.009471 11 37.453 -38.2
## m3 5 25.246 -38.0
## m11 -0.008969 8 27.291 -31.7
## delta weight
## m4 0.00 0.866
## m10 3.77 0.132
## m2 13.43 0.001
## m0 14.15 0.001
## m1 14.81 0.001
## m9 16.87 0.000
## m12 17.46 0.000
## m3 17.70 0.000
## m11 23.97 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m4 1.665 -0.07614 0.001014
## m5 1.485 -0.04104 0.001523
## m2 1.608 -0.06437 0.1255
## m3 1.553 -0.05148 0.0155
## m1 1.553 7.568e-05 -0.05185
## m6 1.577 -0.05503
## lst_wet df logLik AICc delta weight
## m4 4 32.702 -55.8 0.00 0.231
## m5 4 32.568 -55.5 0.27 0.202
## m2 4 32.437 -55.3 0.53 0.177
## m3 4 32.130 -54.7 1.14 0.131
## m1 4 32.130 -54.7 1.14 0.130
## m6 -8.377e-05 4 32.114 -54.6 1.18 0.128
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m4 1.665 -0.07614 0.001014
## m5 1.485 -0.04104 0.001523
## m2 1.608 -0.06437 0.1255
## m3 1.553 -0.05148 0.0155
## m1 1.553 7.568e-05 -0.05185
## m6 1.577 -0.05503
## lst_wet df logLik AICc delta weight
## m4 4 32.702 -55.8 0.00 0.231
## m5 4 32.568 -55.5 0.27 0.202
## m2 4 32.437 -55.3 0.53 0.177
## m3 4 32.130 -54.7 1.14 0.131
## m1 4 32.130 -54.7 1.14 0.130
## m6 -8.377e-05 4 32.114 -54.6 1.18 0.128
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m4 1.665 -0.07614 0.001014
## m5 1.485 -0.04104 0.001523
## m2 1.608 -0.06437 0.1255
## m3 1.553 -0.05148 0.0155
## m1 1.553 7.568e-05 -0.05185
## m6 1.577 -0.05503
## lst_wet df logLik AICc delta weight
## m4 4 32.702 -55.8 0.00 0.231
## m5 4 32.568 -55.5 0.27 0.202
## m2 4 32.437 -55.3 0.53 0.177
## m3 4 32.130 -54.7 1.14 0.131
## m1 4 32.130 -54.7 1.14 0.130
## m6 -8.377e-05 4 32.114 -54.6 1.18 0.128
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m4 1.665 -0.07614 0.001014
## m5 1.485 -0.04104 0.001523
## m2 1.608 -0.06437 0.1255
## m3 1.553 -0.05148 0.0155
## m1 1.553 7.568e-05 -0.05185
## m6 1.577 -0.05503
## lst_wet df logLik AICc delta weight
## m4 4 32.702 -55.8 0.00 0.231
## m5 4 32.568 -55.5 0.27 0.202
## m2 4 32.437 -55.3 0.53 0.177
## m3 4 32.130 -54.7 1.14 0.131
## m1 4 32.130 -54.7 1.14 0.130
## m6 -8.377e-05 4 32.114 -54.6 1.18 0.128
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m4 1.665 -0.07614 0.001014
## m5 1.485 -0.04104 0.001523
## m2 1.608 -0.06437 0.1255
## m3 1.553 -0.05148 0.0155
## m1 1.553 7.568e-05 -0.05185
## m6 1.577 -0.05503
## lst_wet df logLik AICc delta weight
## m4 4 32.702 -55.8 0.00 0.231
## m5 4 32.568 -55.5 0.27 0.202
## m2 4 32.437 -55.3 0.53 0.177
## m3 4 32.130 -54.7 1.14 0.131
## m1 4 32.130 -54.7 1.14 0.130
## m6 -8.377e-05 4 32.114 -54.6 1.18 0.128
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m2 2.561 -0.1228 -0.758
## m6 2.746 -0.1623
## m4 2.679 -0.1364 -0.001112
## m5 2.578 -0.1313 -0.003044
## m3 2.603 -0.1353 -0.06928
## m1 2.639 1.472e-05 -0.1411
## lst_wet df logLik AICc delta weight
## m2 4 20.724 -31.8 0.00 0.265
## m6 0.001337 4 20.522 -31.4 0.40 0.216
## m4 4 20.278 -30.9 0.89 0.169
## m5 4 20.053 -30.4 1.34 0.135
## m3 4 19.866 -30.1 1.72 0.112
## m1 4 19.768 -29.9 1.91 0.102
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m2 2.561 -0.1228 -0.758
## m6 2.746 -0.1623
## m4 2.679 -0.1364 -0.001112
## m5 2.578 -0.1313 -0.003044
## m3 2.603 -0.1353 -0.06928
## m1 2.639 1.472e-05 -0.1411
## lst_wet df logLik AICc delta weight
## m2 4 20.724 -31.8 0.00 0.265
## m6 0.001337 4 20.522 -31.4 0.40 0.216
## m4 4 20.278 -30.9 0.89 0.169
## m5 4 20.053 -30.4 1.34 0.135
## m3 4 19.866 -30.1 1.72 0.112
## m1 4 19.768 -29.9 1.91 0.102
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m2 2.561 -0.1228 -0.758
## m6 2.746 -0.1623
## m4 2.679 -0.1364 -0.001112
## m5 2.578 -0.1313 -0.003044
## m3 2.603 -0.1353 -0.06928
## m1 2.639 1.472e-05 -0.1411
## lst_wet df logLik AICc delta weight
## m2 4 20.724 -31.8 0.00 0.265
## m6 0.001337 4 20.522 -31.4 0.40 0.216
## m4 4 20.278 -30.9 0.89 0.169
## m5 4 20.053 -30.4 1.34 0.135
## m3 4 19.866 -30.1 1.72 0.112
## m1 4 19.768 -29.9 1.91 0.102
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m2 2.561 -0.1228 -0.758
## m6 2.746 -0.1623
## m4 2.679 -0.1364 -0.001112
## m5 2.578 -0.1313 -0.003044
## m3 2.603 -0.1353 -0.06928
## m1 2.639 1.472e-05 -0.1411
## lst_wet df logLik AICc delta weight
## m2 4 20.724 -31.8 0.00 0.265
## m6 0.001337 4 20.522 -31.4 0.40 0.216
## m4 4 20.278 -30.9 0.89 0.169
## m5 4 20.053 -30.4 1.34 0.135
## m3 4 19.866 -30.1 1.72 0.112
## m1 4 19.768 -29.9 1.91 0.102
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m4 0.7899 0.10620 -0.001544
## m5 1.0290 0.04865 -0.003963
## m3 1.0150 0.05103 -0.1236
## m2 0.9789 0.05694 0.005968
## m6 0.9805 0.05702
## m1 0.9817 -3.174e-05 0.05683
## lst_wet df logLik AICc delta weight
## m4 4 43.063 -76.5 0.00 0.377
## m5 4 42.180 -74.8 1.77 0.156
## m3 4 41.985 -74.4 2.16 0.128
## m2 4 41.855 -74.1 2.42 0.113
## m6 -0.0001047 4 41.854 -74.1 2.42 0.113
## m1 4 41.852 -74.1 2.42 0.112
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m4 0.7899 0.10620 -0.001544
## m5 1.0290 0.04865 -0.003963
## m3 1.0150 0.05103 -0.1236
## m2 0.9789 0.05694 0.005968
## m6 0.9805 0.05702
## m1 0.9817 -3.174e-05 0.05683
## lst_wet df logLik AICc delta weight
## m4 4 43.063 -76.5 0.00 0.377
## m5 4 42.180 -74.8 1.77 0.156
## m3 4 41.985 -74.4 2.16 0.128
## m2 4 41.855 -74.1 2.42 0.113
## m6 -0.0001047 4 41.854 -74.1 2.42 0.113
## m1 4 41.852 -74.1 2.42 0.112
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m4 0.7899 0.10620 -0.001544
## m5 1.0290 0.04865 -0.003963
## m3 1.0150 0.05103 -0.1236
## m2 0.9789 0.05694 0.005968
## m6 0.9805 0.05702
## m1 0.9817 -3.174e-05 0.05683
## lst_wet df logLik AICc delta weight
## m4 4 43.063 -76.5 0.00 0.377
## m5 4 42.180 -74.8 1.77 0.156
## m3 4 41.985 -74.4 2.16 0.128
## m2 4 41.855 -74.1 2.42 0.113
## m6 -0.0001047 4 41.854 -74.1 2.42 0.113
## m1 4 41.852 -74.1 2.42 0.112
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m5 0.3382 0.18040 0.03945
## m4 0.4091 0.19830 -0.005406
## m3 0.2763 0.19480 0.2222
## m1 0.2773 0.001783 0.18280
## m6 0.8450 0.09020
## m2 0.8621 0.08973 0.01891
## lst_wet df logLik AICc delta weight
## m5 4 32.128 -54.7 0.00 0.547
## m4 4 30.844 -52.1 2.57 0.152
## m3 4 30.805 -52.0 2.65 0.146
## m1 4 30.783 -52.0 2.69 0.143
## m6 0.001179 4 27.933 -46.3 8.39 0.008
## m2 4 27.358 -45.1 9.54 0.005
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m5 0.3382 0.18040 0.03945
## m4 0.4091 0.19830 -0.005406
## m3 0.2763 0.19480 0.2222
## m1 0.2773 0.001783 0.18280
## m6 0.8450 0.09020
## m2 0.8621 0.08973 0.01891
## lst_wet df logLik AICc delta weight
## m5 4 32.128 -54.7 0.00 0.547
## m4 4 30.844 -52.1 2.57 0.152
## m3 4 30.805 -52.0 2.65 0.146
## m1 4 30.783 -52.0 2.69 0.143
## m6 0.001179 4 27.933 -46.3 8.39 0.008
## m2 4 27.358 -45.1 9.54 0.005
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m1 1.368 0.0006329 -0.01786
## m5 1.414 -0.02236 0.00523
## m4 1.422 -0.02080 -0.0003044
## m2 1.432 -0.02486 -0.02072
## m3 1.433 -0.02531 -0.008356
## m6 1.432 -0.02508
## lst_wet df logLik AICc delta weight
## m1 4 24.350 -39.1 0.00 0.182
## m5 4 24.265 -38.9 0.17 0.167
## m4 4 24.258 -38.9 0.18 0.166
## m2 4 24.237 -38.9 0.23 0.162
## m3 4 24.236 -38.9 0.23 0.162
## m6 -1.482e-05 4 24.235 -38.9 0.23 0.162
## Models ranked by AICc(x)
## Error in nrow(concord.out): object 'concord.out' not found
sites<-c("puertorico", "macae", "frenchguiana", "costarica", "colombia", "argentina", "cardoso")
concord.out9c<-concord.magic(sites, "scaled.n15.bromeliad.final", no126data, 10, 2, "gaussian")
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m1 1.417 -0.026640 0.02762
## m0 1.571 -0.054190
## m3 1.324 -0.012770 0.04290 0.01360
## m2 1.530 -0.047110 0.02392
## m10 1.083 0.034730 0.08012 0.02240 -0.07387
## m9 1.343 -0.013950 0.02827 0.02021
## m4 1.525 -0.044800 0.02404 -0.02925
## m11 1.255 -0.001098 0.04410 0.01451 0.01417
## m12 0.760 0.084370 0.12890 0.02150 0.03909 -0.02230
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m1
## m0
## m3
## m2
## m10 -0.02385 -0.1541
## m9 -0.02230
## m4
## m11 -0.01533
## m12 -0.02175 -0.2373
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik AICc
## m1 4 33.979 -58.4
## m0 3 32.111 -57.3
## m3 5 34.487 -56.5
## m2 4 32.509 -55.4
## m10 8 38.825 -54.8
## m9 6 34.947 -54.2
## m4 5 32.607 -52.7
## m11 0.006887 8 35.590 -48.3
## m12 0.003435 -0.06497 11 40.702 -44.7
## delta weight
## m1 0.00 0.389
## m0 1.06 0.229
## m3 1.89 0.151
## m2 2.94 0.089
## m10 3.57 0.065
## m9 4.12 0.050
## m4 5.64 0.023
## m11 10.04 0.003
## m12 13.62 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m1 1.417 -0.026640 0.02762
## m0 1.571 -0.054190
## m3 1.324 -0.012770 0.04290 0.01360
## m2 1.530 -0.047110 0.02392
## m10 1.083 0.034730 0.08012 0.02240 -0.07387
## m9 1.343 -0.013950 0.02827 0.02021
## m4 1.525 -0.044800 0.02404 -0.02925
## m11 1.255 -0.001098 0.04410 0.01451 0.01417
## m12 0.760 0.084370 0.12890 0.02150 0.03909 -0.02230
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m1
## m0
## m3
## m2
## m10 -0.02385 -0.1541
## m9 -0.02230
## m4
## m11 -0.01533
## m12 -0.02175 -0.2373
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik AICc
## m1 4 33.979 -58.4
## m0 3 32.111 -57.3
## m3 5 34.487 -56.5
## m2 4 32.509 -55.4
## m10 8 38.825 -54.8
## m9 6 34.947 -54.2
## m4 5 32.607 -52.7
## m11 0.006887 8 35.590 -48.3
## m12 0.003435 -0.06497 11 40.702 -44.7
## delta weight
## m1 0.00 0.389
## m0 1.06 0.229
## m3 1.89 0.151
## m2 2.94 0.089
## m10 3.57 0.065
## m9 4.12 0.050
## m4 5.64 0.023
## m11 10.04 0.003
## m12 13.62 0.000
## Models ranked by AICc(x)
## Warning in matrix(value, n, p): data length [11] is not a sub-multiple or
## multiple of the number of columns [10]
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m1 1.417 -0.026640 0.02762
## m0 1.571 -0.054190
## m3 1.324 -0.012770 0.04290 0.01360
## m2 1.530 -0.047110 0.02392
## m10 1.083 0.034730 0.08012 0.02240 -0.07387
## m9 1.343 -0.013950 0.02827 0.02021
## m4 1.525 -0.044800 0.02404 -0.02925
## m11 1.255 -0.001098 0.04410 0.01451 0.01417
## m12 0.760 0.084370 0.12890 0.02150 0.03909 -0.02230
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m1
## m0
## m3
## m2
## m10 -0.02385 -0.1541
## m9 -0.02230
## m4
## m11 -0.01533
## m12 -0.02175 -0.2373
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik AICc
## m1 4 33.979 -58.4
## m0 3 32.111 -57.3
## m3 5 34.487 -56.5
## m2 4 32.509 -55.4
## m10 8 38.825 -54.8
## m9 6 34.947 -54.2
## m4 5 32.607 -52.7
## m11 0.006887 8 35.590 -48.3
## m12 0.003435 -0.06497 11 40.702 -44.7
## delta weight
## m1 0.00 0.389
## m0 1.06 0.229
## m3 1.89 0.151
## m2 2.94 0.089
## m10 3.57 0.065
## m9 4.12 0.050
## m4 5.64 0.023
## m11 10.04 0.003
## m12 13.62 0.000
## Models ranked by AICc(x)
## Warning in matrix(value, n, p): data length [11] is not a sub-multiple or
## multiple of the number of columns [10]
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m1 1.417 -0.026640 0.02762
## m0 1.571 -0.054190
## m3 1.324 -0.012770 0.04290 0.01360
## m2 1.530 -0.047110 0.02392
## m10 1.083 0.034730 0.08012 0.02240 -0.07387
## m9 1.343 -0.013950 0.02827 0.02021
## m4 1.525 -0.044800 0.02404 -0.02925
## m11 1.255 -0.001098 0.04410 0.01451 0.01417
## m12 0.760 0.084370 0.12890 0.02150 0.03909 -0.02230
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m1
## m0
## m3
## m2
## m10 -0.02385 -0.1541
## m9 -0.02230
## m4
## m11 -0.01533
## m12 -0.02175 -0.2373
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik AICc
## m1 4 33.979 -58.4
## m0 3 32.111 -57.3
## m3 5 34.487 -56.5
## m2 4 32.509 -55.4
## m10 8 38.825 -54.8
## m9 6 34.947 -54.2
## m4 5 32.607 -52.7
## m11 0.006887 8 35.590 -48.3
## m12 0.003435 -0.06497 11 40.702 -44.7
## delta weight
## m1 0.00 0.389
## m0 1.06 0.229
## m3 1.89 0.151
## m2 2.94 0.089
## m10 3.57 0.065
## m9 4.12 0.050
## m4 5.64 0.023
## m11 10.04 0.003
## m12 13.62 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m1 1.417 -0.026640 0.02762
## m0 1.571 -0.054190
## m3 1.324 -0.012770 0.04290 0.01360
## m2 1.530 -0.047110 0.02392
## m10 1.083 0.034730 0.08012 0.02240 -0.07387
## m9 1.343 -0.013950 0.02827 0.02021
## m4 1.525 -0.044800 0.02404 -0.02925
## m11 1.255 -0.001098 0.04410 0.01451 0.01417
## m12 0.760 0.084370 0.12890 0.02150 0.03909 -0.02230
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m1
## m0
## m3
## m2
## m10 -0.02385 -0.1541
## m9 -0.02230
## m4
## m11 -0.01533
## m12 -0.02175 -0.2373
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik AICc
## m1 4 33.979 -58.4
## m0 3 32.111 -57.3
## m3 5 34.487 -56.5
## m2 4 32.509 -55.4
## m10 8 38.825 -54.8
## m9 6 34.947 -54.2
## m4 5 32.607 -52.7
## m11 0.006887 8 35.590 -48.3
## m12 0.003435 -0.06497 11 40.702 -44.7
## delta weight
## m1 0.00 0.389
## m0 1.06 0.229
## m3 1.89 0.151
## m2 2.94 0.089
## m10 3.57 0.065
## m9 4.12 0.050
## m4 5.64 0.023
## m11 10.04 0.003
## m12 13.62 0.000
## Models ranked by AICc(x)
## Warning in matrix(value, n, p): data length [11] is not a sub-multiple or
## multiple of the number of columns [10]
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m1 1.417 -0.026640 0.02762
## m0 1.571 -0.054190
## m3 1.324 -0.012770 0.04290 0.01360
## m2 1.530 -0.047110 0.02392
## m10 1.083 0.034730 0.08012 0.02240 -0.07387
## m9 1.343 -0.013950 0.02827 0.02021
## m4 1.525 -0.044800 0.02404 -0.02925
## m11 1.255 -0.001098 0.04410 0.01451 0.01417
## m12 0.760 0.084370 0.12890 0.02150 0.03909 -0.02230
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m1
## m0
## m3
## m2
## m10 -0.02385 -0.1541
## m9 -0.02230
## m4
## m11 -0.01533
## m12 -0.02175 -0.2373
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik AICc
## m1 4 33.979 -58.4
## m0 3 32.111 -57.3
## m3 5 34.487 -56.5
## m2 4 32.509 -55.4
## m10 8 38.825 -54.8
## m9 6 34.947 -54.2
## m4 5 32.607 -52.7
## m11 0.006887 8 35.590 -48.3
## m12 0.003435 -0.06497 11 40.702 -44.7
## delta weight
## m1 0.00 0.389
## m0 1.06 0.229
## m3 1.89 0.151
## m2 2.94 0.089
## m10 3.57 0.065
## m9 4.12 0.050
## m4 5.64 0.023
## m11 10.04 0.003
## m12 13.62 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m1 2.198 -0.07838 -0.0351200
## m0 2.637 -0.14070
## m2 2.828 -0.16840 0.020980
## m3 2.311 -0.09360 -0.0444900 -0.009074
## m9 2.114 -0.06620 -0.0353600 0.001038
## m4 3.231 -0.22300 0.024910 -0.08432
## m10 2.457 -0.11160 0.0005586 0.004896 -0.09551
## m11 2.147 -0.06978 -0.0447400 -0.015390 -0.008959
## m12 1.233 0.05815 0.0491000 -0.027270 0.048230 0.09253
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m1
## m0
## m2
## m3
## m9 -0.05916
## m4
## m10 -0.05803 -0.1099
## m11 -0.04169
## m12 -0.04052 -0.2948
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik AICc
## m1 4 21.159 -32.7
## m0 3 19.768 -32.6
## m2 4 19.893 -30.1
## m3 5 21.258 -29.9
## m9 6 22.697 -29.6
## m4 5 20.225 -27.8
## m10 8 23.845 -24.5
## m11 0.01655 8 22.919 -22.6
## m12 0.02017 -0.1736 11 26.193 -14.9
## delta weight
## m1 0.00 0.354
## m0 0.08 0.341
## m2 2.53 0.100
## m3 2.74 0.090
## m9 3.08 0.076
## m4 4.81 0.032
## m10 8.16 0.006
## m11 10.01 0.002
## m12 17.79 0.000
## Models ranked by AICc(x)
## Warning in matrix(value, n, p): data length [11] is not a sub-multiple or
## multiple of the number of columns [10]
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m1 2.198 -0.07838 -0.0351200
## m0 2.637 -0.14070
## m2 2.828 -0.16840 0.020980
## m3 2.311 -0.09360 -0.0444900 -0.009074
## m9 2.114 -0.06620 -0.0353600 0.001038
## m4 3.231 -0.22300 0.024910 -0.08432
## m10 2.457 -0.11160 0.0005586 0.004896 -0.09551
## m11 2.147 -0.06978 -0.0447400 -0.015390 -0.008959
## m12 1.233 0.05815 0.0491000 -0.027270 0.048230 0.09253
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m1
## m0
## m2
## m3
## m9 -0.05916
## m4
## m10 -0.05803 -0.1099
## m11 -0.04169
## m12 -0.04052 -0.2948
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik AICc
## m1 4 21.159 -32.7
## m0 3 19.768 -32.6
## m2 4 19.893 -30.1
## m3 5 21.258 -29.9
## m9 6 22.697 -29.6
## m4 5 20.225 -27.8
## m10 8 23.845 -24.5
## m11 0.01655 8 22.919 -22.6
## m12 0.02017 -0.1736 11 26.193 -14.9
## delta weight
## m1 0.00 0.354
## m0 0.08 0.341
## m2 2.53 0.100
## m3 2.74 0.090
## m9 3.08 0.076
## m4 4.81 0.032
## m10 8.16 0.006
## m11 10.01 0.002
## m12 17.79 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m1 2.198 -0.07838 -0.0351200
## m0 2.637 -0.14070
## m2 2.828 -0.16840 0.020980
## m3 2.311 -0.09360 -0.0444900 -0.009074
## m9 2.114 -0.06620 -0.0353600 0.001038
## m4 3.231 -0.22300 0.024910 -0.08432
## m10 2.457 -0.11160 0.0005586 0.004896 -0.09551
## m11 2.147 -0.06978 -0.0447400 -0.015390 -0.008959
## m12 1.233 0.05815 0.0491000 -0.027270 0.048230 0.09253
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m1
## m0
## m2
## m3
## m9 -0.05916
## m4
## m10 -0.05803 -0.1099
## m11 -0.04169
## m12 -0.04052 -0.2948
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik AICc
## m1 4 21.159 -32.7
## m0 3 19.768 -32.6
## m2 4 19.893 -30.1
## m3 5 21.258 -29.9
## m9 6 22.697 -29.6
## m4 5 20.225 -27.8
## m10 8 23.845 -24.5
## m11 0.01655 8 22.919 -22.6
## m12 0.02017 -0.1736 11 26.193 -14.9
## delta weight
## m1 0.00 0.354
## m0 0.08 0.341
## m2 2.53 0.100
## m3 2.74 0.090
## m9 3.08 0.076
## m4 4.81 0.032
## m10 8.16 0.006
## m11 10.01 0.002
## m12 17.79 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m1 2.198 -0.07838 -0.0351200
## m0 2.637 -0.14070
## m2 2.828 -0.16840 0.020980
## m3 2.311 -0.09360 -0.0444900 -0.009074
## m9 2.114 -0.06620 -0.0353600 0.001038
## m4 3.231 -0.22300 0.024910 -0.08432
## m10 2.457 -0.11160 0.0005586 0.004896 -0.09551
## m11 2.147 -0.06978 -0.0447400 -0.015390 -0.008959
## m12 1.233 0.05815 0.0491000 -0.027270 0.048230 0.09253
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m1
## m0
## m2
## m3
## m9 -0.05916
## m4
## m10 -0.05803 -0.1099
## m11 -0.04169
## m12 -0.04052 -0.2948
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik AICc
## m1 4 21.159 -32.7
## m0 3 19.768 -32.6
## m2 4 19.893 -30.1
## m3 5 21.258 -29.9
## m9 6 22.697 -29.6
## m4 5 20.225 -27.8
## m10 8 23.845 -24.5
## m11 0.01655 8 22.919 -22.6
## m12 0.02017 -0.1736 11 26.193 -14.9
## delta weight
## m1 0.00 0.354
## m0 0.08 0.341
## m2 2.53 0.100
## m3 2.74 0.090
## m9 3.08 0.076
## m4 4.81 0.032
## m10 8.16 0.006
## m11 10.01 0.002
## m12 17.79 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m1 2.198 -0.07838 -0.0351200
## m0 2.637 -0.14070
## m2 2.828 -0.16840 0.020980
## m3 2.311 -0.09360 -0.0444900 -0.009074
## m9 2.114 -0.06620 -0.0353600 0.001038
## m4 3.231 -0.22300 0.024910 -0.08432
## m10 2.457 -0.11160 0.0005586 0.004896 -0.09551
## m11 2.147 -0.06978 -0.0447400 -0.015390 -0.008959
## m12 1.233 0.05815 0.0491000 -0.027270 0.048230 0.09253
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m1
## m0
## m2
## m3
## m9 -0.05916
## m4
## m10 -0.05803 -0.1099
## m11 -0.04169
## m12 -0.04052 -0.2948
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik AICc
## m1 4 21.159 -32.7
## m0 3 19.768 -32.6
## m2 4 19.893 -30.1
## m3 5 21.258 -29.9
## m9 6 22.697 -29.6
## m4 5 20.225 -27.8
## m10 8 23.845 -24.5
## m11 0.01655 8 22.919 -22.6
## m12 0.02017 -0.1736 11 26.193 -14.9
## delta weight
## m1 0.00 0.354
## m0 0.08 0.341
## m2 2.53 0.100
## m3 2.74 0.090
## m9 3.08 0.076
## m4 4.81 0.032
## m10 8.16 0.006
## m11 10.01 0.002
## m12 17.79 0.000
## Models ranked by AICc(x)
## Warning in matrix(value, n, p): data length [11] is not a sub-multiple or
## multiple of the number of columns [10]
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m4 1.0940 0.03137 -0.05003 0.07771
## m2 1.2000 0.01645 -0.05262
## m0 0.9760 0.05774
## m9 1.2050 0.01538 -0.0007095 -0.04932
## m1 0.9696 0.05883 -0.0021280
## m10 1.0260 0.04308 -0.0162900 -0.04462 0.09162
## m3 0.9809 0.05779 -0.0087130 -0.006182
## m11 1.2070 0.01620 -0.0078130 -0.06261 -0.006609
## m12 1.0110 0.04770 -0.0300600 -0.05794 -0.012340 0.07576
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m4
## m2
## m0
## m9 0.01426
## m1
## m10 0.01376 0.04581
## m3
## m11 0.03003
## m12 0.03025 0.06732
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik AICc
## m4 5 47.102 -81.7
## m2 4 45.444 -81.3
## m0 3 41.851 -76.8
## m9 6 45.836 -76.0
## m1 4 41.872 -74.1
## m10 8 48.222 -73.6
## m3 5 42.056 -71.6
## m11 0.01474 8 46.561 -70.3
## m12 0.01548 0.01891 11 49.234 -61.8
## delta weight
## m4 0.00 0.499
## m2 0.42 0.405
## m0 4.92 0.042
## m9 5.68 0.029
## m1 7.56 0.011
## m10 8.12 0.009
## m3 10.09 0.003
## m11 11.44 0.002
## m12 19.90 0.000
## Models ranked by AICc(x)
## Warning in matrix(value, n, p): data length [11] is not a sub-multiple or
## multiple of the number of columns [10]
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m4 1.0940 0.03137 -0.05003 0.07771
## m2 1.2000 0.01645 -0.05262
## m0 0.9760 0.05774
## m9 1.2050 0.01538 -0.0007095 -0.04932
## m1 0.9696 0.05883 -0.0021280
## m10 1.0260 0.04308 -0.0162900 -0.04462 0.09162
## m3 0.9809 0.05779 -0.0087130 -0.006182
## m11 1.2070 0.01620 -0.0078130 -0.06261 -0.006609
## m12 1.0110 0.04770 -0.0300600 -0.05794 -0.012340 0.07576
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m4
## m2
## m0
## m9 0.01426
## m1
## m10 0.01376 0.04581
## m3
## m11 0.03003
## m12 0.03025 0.06732
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik AICc
## m4 5 47.102 -81.7
## m2 4 45.444 -81.3
## m0 3 41.851 -76.8
## m9 6 45.836 -76.0
## m1 4 41.872 -74.1
## m10 8 48.222 -73.6
## m3 5 42.056 -71.6
## m11 0.01474 8 46.561 -70.3
## m12 0.01548 0.01891 11 49.234 -61.8
## delta weight
## m4 0.00 0.499
## m2 0.42 0.405
## m0 4.92 0.042
## m9 5.68 0.029
## m1 7.56 0.011
## m10 8.12 0.009
## m3 10.09 0.003
## m11 11.44 0.002
## m12 19.90 0.000
## Models ranked by AICc(x)
## Warning in matrix(value, n, p): data length [11] is not a sub-multiple or
## multiple of the number of columns [10]
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m4 1.0940 0.03137 -0.05003 0.07771
## m2 1.2000 0.01645 -0.05262
## m0 0.9760 0.05774
## m9 1.2050 0.01538 -0.0007095 -0.04932
## m1 0.9696 0.05883 -0.0021280
## m10 1.0260 0.04308 -0.0162900 -0.04462 0.09162
## m3 0.9809 0.05779 -0.0087130 -0.006182
## m11 1.2070 0.01620 -0.0078130 -0.06261 -0.006609
## m12 1.0110 0.04770 -0.0300600 -0.05794 -0.012340 0.07576
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m4
## m2
## m0
## m9 0.01426
## m1
## m10 0.01376 0.04581
## m3
## m11 0.03003
## m12 0.03025 0.06732
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik AICc
## m4 5 47.102 -81.7
## m2 4 45.444 -81.3
## m0 3 41.851 -76.8
## m9 6 45.836 -76.0
## m1 4 41.872 -74.1
## m10 8 48.222 -73.6
## m3 5 42.056 -71.6
## m11 0.01474 8 46.561 -70.3
## m12 0.01548 0.01891 11 49.234 -61.8
## delta weight
## m4 0.00 0.499
## m2 0.42 0.405
## m0 4.92 0.042
## m9 5.68 0.029
## m1 7.56 0.011
## m10 8.12 0.009
## m3 10.09 0.003
## m11 11.44 0.002
## m12 19.90 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m4 1.0940 0.03137 -0.05003 0.07771
## m2 1.2000 0.01645 -0.05262
## m0 0.9760 0.05774
## m9 1.2050 0.01538 -0.0007095 -0.04932
## m1 0.9696 0.05883 -0.0021280
## m10 1.0260 0.04308 -0.0162900 -0.04462 0.09162
## m3 0.9809 0.05779 -0.0087130 -0.006182
## m11 1.2070 0.01620 -0.0078130 -0.06261 -0.006609
## m12 1.0110 0.04770 -0.0300600 -0.05794 -0.012340 0.07576
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m4
## m2
## m0
## m9 0.01426
## m1
## m10 0.01376 0.04581
## m3
## m11 0.03003
## m12 0.03025 0.06732
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik AICc
## m4 5 47.102 -81.7
## m2 4 45.444 -81.3
## m0 3 41.851 -76.8
## m9 6 45.836 -76.0
## m1 4 41.872 -74.1
## m10 8 48.222 -73.6
## m3 5 42.056 -71.6
## m11 0.01474 8 46.561 -70.3
## m12 0.01548 0.01891 11 49.234 -61.8
## delta weight
## m4 0.00 0.499
## m2 0.42 0.405
## m0 4.92 0.042
## m9 5.68 0.029
## m1 7.56 0.011
## m10 8.12 0.009
## m3 10.09 0.003
## m11 11.44 0.002
## m12 19.90 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 0.8679 0.08931
## m2 0.8810 0.08678 -0.04595
## m4 0.8868 0.09148 -0.04592 -0.09389
## m1 0.8404 0.09516 0.01057
## m9 0.5935 0.14310 0.01146 -0.03604
## m3 0.8198 0.09749 0.02069 0.0094140
## m10 0.4490 0.17850 0.04102 -0.03464 -0.11920
## m11 0.5937 0.14130 0.02189 -0.04083 0.0097660
## m12 0.4044 0.18730 0.04115 -0.03584 -0.0004945 -0.15010
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m2
## m4
## m1
## m9 0.03900
## m3
## m10 0.04379 -0.09020
## m11 0.04431
## m12 0.04680 -0.05621
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik AICc
## m0 3 27.351 -47.8
## m2 4 28.464 -47.3
## m4 5 29.255 -46.0
## m1 4 27.547 -45.5
## m9 6 29.470 -43.3
## m3 5 27.711 -42.9
## m10 8 31.231 -39.6
## m11 0.005184 8 29.689 -36.5
## m12 0.001701 0.03319 11 31.609 -26.6
## delta weight
## m0 0.00 0.364
## m2 0.45 0.291
## m4 1.77 0.151
## m1 2.28 0.116
## m9 4.49 0.039
## m3 4.86 0.032
## m10 8.17 0.006
## m11 11.26 0.001
## m12 21.23 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 0.8679 0.08931
## m2 0.8810 0.08678 -0.04595
## m4 0.8868 0.09148 -0.04592 -0.09389
## m1 0.8404 0.09516 0.01057
## m9 0.5935 0.14310 0.01146 -0.03604
## m3 0.8198 0.09749 0.02069 0.0094140
## m10 0.4490 0.17850 0.04102 -0.03464 -0.11920
## m11 0.5937 0.14130 0.02189 -0.04083 0.0097660
## m12 0.4044 0.18730 0.04115 -0.03584 -0.0004945 -0.15010
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m2
## m4
## m1
## m9 0.03900
## m3
## m10 0.04379 -0.09020
## m11 0.04431
## m12 0.04680 -0.05621
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik AICc
## m0 3 27.351 -47.8
## m2 4 28.464 -47.3
## m4 5 29.255 -46.0
## m1 4 27.547 -45.5
## m9 6 29.470 -43.3
## m3 5 27.711 -42.9
## m10 8 31.231 -39.6
## m11 0.005184 8 29.689 -36.5
## m12 0.001701 0.03319 11 31.609 -26.6
## delta weight
## m0 0.00 0.364
## m2 0.45 0.291
## m4 1.77 0.151
## m1 2.28 0.116
## m9 4.49 0.039
## m3 4.86 0.032
## m10 8.17 0.006
## m11 11.26 0.001
## m12 21.23 0.000
## Models ranked by AICc(x)
## Warning in matrix(value, n, p): data length [11] is not a sub-multiple or
## multiple of the number of columns [10]
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 0.8679 0.08931
## m2 0.8810 0.08678 -0.04595
## m4 0.8868 0.09148 -0.04592 -0.09389
## m1 0.8404 0.09516 0.01057
## m9 0.5935 0.14310 0.01146 -0.03604
## m3 0.8198 0.09749 0.02069 0.0094140
## m10 0.4490 0.17850 0.04102 -0.03464 -0.11920
## m11 0.5937 0.14130 0.02189 -0.04083 0.0097660
## m12 0.4044 0.18730 0.04115 -0.03584 -0.0004945 -0.15010
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m2
## m4
## m1
## m9 0.03900
## m3
## m10 0.04379 -0.09020
## m11 0.04431
## m12 0.04680 -0.05621
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik AICc
## m0 3 27.351 -47.8
## m2 4 28.464 -47.3
## m4 5 29.255 -46.0
## m1 4 27.547 -45.5
## m9 6 29.470 -43.3
## m3 5 27.711 -42.9
## m10 8 31.231 -39.6
## m11 0.005184 8 29.689 -36.5
## m12 0.001701 0.03319 11 31.609 -26.6
## delta weight
## m0 0.00 0.364
## m2 0.45 0.291
## m4 1.77 0.151
## m1 2.28 0.116
## m9 4.49 0.039
## m3 4.86 0.032
## m10 8.17 0.006
## m11 11.26 0.001
## m12 21.23 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m4 1.776 -0.10210 -0.09235 0.3233
## m10 1.772 -0.10360 -0.04786 -0.09568 0.3391
## m2 1.570 -0.04889 -0.06799
## m0 1.431 -0.02508
## m1 1.442 -0.02799 -0.02614
## m9 1.582 -0.05222 -0.02715 -0.07246
## m12 1.761 -0.10260 -0.04167 -0.09186 0.005819 0.3467
## m3 1.441 -0.02806 -0.02495 0.001112
## m11 1.576 -0.05137 -0.02536 -0.06390 0.001635
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m4
## m10 -0.01088 0.05794
## m2
## m0
## m1
## m9 -0.01204
## m12 -0.01487 0.04806
## m3
## m11 -0.02167
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik AICc
## m4 5 34.098 -55.7
## m10 8 37.394 -51.9
## m2 4 25.931 -42.3
## m0 3 24.235 -41.5
## m1 4 25.244 -40.9
## m9 6 27.240 -38.8
## m12 -0.003704 -0.009471 11 37.453 -38.2
## m3 5 25.246 -38.0
## m11 -0.008969 8 27.291 -31.7
## delta weight
## m4 0.00 0.866
## m10 3.77 0.132
## m2 13.43 0.001
## m0 14.15 0.001
## m1 14.81 0.001
## m9 16.87 0.000
## m12 17.46 0.000
## m3 17.70 0.000
## m11 23.97 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m4 1.776 -0.10210 -0.09235 0.3233
## m10 1.772 -0.10360 -0.04786 -0.09568 0.3391
## m2 1.570 -0.04889 -0.06799
## m0 1.431 -0.02508
## m1 1.442 -0.02799 -0.02614
## m9 1.582 -0.05222 -0.02715 -0.07246
## m12 1.761 -0.10260 -0.04167 -0.09186 0.005819 0.3467
## m3 1.441 -0.02806 -0.02495 0.001112
## m11 1.576 -0.05137 -0.02536 -0.06390 0.001635
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m4
## m10 -0.01088 0.05794
## m2
## m0
## m1
## m9 -0.01204
## m12 -0.01487 0.04806
## m3
## m11 -0.02167
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik AICc
## m4 5 34.098 -55.7
## m10 8 37.394 -51.9
## m2 4 25.931 -42.3
## m0 3 24.235 -41.5
## m1 4 25.244 -40.9
## m9 6 27.240 -38.8
## m12 -0.003704 -0.009471 11 37.453 -38.2
## m3 5 25.246 -38.0
## m11 -0.008969 8 27.291 -31.7
## delta weight
## m4 0.00 0.866
## m10 3.77 0.132
## m2 13.43 0.001
## m0 14.15 0.001
## m1 14.81 0.001
## m9 16.87 0.000
## m12 17.46 0.000
## m3 17.70 0.000
## m11 23.97 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m10 0.9732 0.10500 0.06185 0.07751 -0.1631
## m9 0.9785 0.09500 0.04676 0.07709
## m1 1.0950 0.07469 0.04533
## m4 1.3420 0.03821 0.06767 -0.1490
## m2 1.3240 0.03312 0.06753
## m3 1.1020 0.07391 0.04247 -0.002620
## m0 1.3700 0.02507
## m11 1.0090 0.09004 0.04390 0.07138 -0.002346
## m12 1.0030 0.10090 0.05457 0.07305 -0.006389 -0.1749
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m10 0.03294 -0.04489
## m9 0.03228
## m1
## m4
## m2
## m3
## m0
## m11 0.03842
## m12 0.03770 -0.03032
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik AICc
## m10 8 37.913 -53.0
## m9 6 34.243 -52.8
## m1 4 30.142 -50.7
## m4 5 31.090 -49.7
## m2 4 28.939 -48.3
## m3 5 30.157 -47.8
## m0 3 26.568 -46.2
## m11 0.006034 8 34.289 -45.7
## m12 0.004702 0.0132 11 37.986 -39.3
## delta weight
## m10 0.00 0.373
## m9 0.14 0.349
## m1 2.28 0.119
## m4 3.29 0.072
## m2 4.69 0.036
## m3 5.15 0.028
## m0 6.76 0.013
## m11 7.25 0.010
## m12 13.66 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m4 1.665 -0.07614 0.001014
## m5 1.485 -0.04104 0.001523
## m2 1.608 -0.06437 0.1255
## m3 1.553 -0.05148 0.0155
## m1 1.553 7.568e-05 -0.05185
## m6 1.577 -0.05503
## lst_wet df logLik AICc delta weight
## m4 4 32.702 -55.8 0.00 0.231
## m5 4 32.568 -55.5 0.27 0.202
## m2 4 32.437 -55.3 0.53 0.177
## m3 4 32.130 -54.7 1.14 0.131
## m1 4 32.130 -54.7 1.14 0.130
## m6 -8.377e-05 4 32.114 -54.6 1.18 0.128
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m4 1.665 -0.07614 0.001014
## m5 1.485 -0.04104 0.001523
## m2 1.608 -0.06437 0.1255
## m3 1.553 -0.05148 0.0155
## m1 1.553 7.568e-05 -0.05185
## m6 1.577 -0.05503
## lst_wet df logLik AICc delta weight
## m4 4 32.702 -55.8 0.00 0.231
## m5 4 32.568 -55.5 0.27 0.202
## m2 4 32.437 -55.3 0.53 0.177
## m3 4 32.130 -54.7 1.14 0.131
## m1 4 32.130 -54.7 1.14 0.130
## m6 -8.377e-05 4 32.114 -54.6 1.18 0.128
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m4 1.665 -0.07614 0.001014
## m5 1.485 -0.04104 0.001523
## m2 1.608 -0.06437 0.1255
## m3 1.553 -0.05148 0.0155
## m1 1.553 7.568e-05 -0.05185
## m6 1.577 -0.05503
## lst_wet df logLik AICc delta weight
## m4 4 32.702 -55.8 0.00 0.231
## m5 4 32.568 -55.5 0.27 0.202
## m2 4 32.437 -55.3 0.53 0.177
## m3 4 32.130 -54.7 1.14 0.131
## m1 4 32.130 -54.7 1.14 0.130
## m6 -8.377e-05 4 32.114 -54.6 1.18 0.128
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m4 1.665 -0.07614 0.001014
## m5 1.485 -0.04104 0.001523
## m2 1.608 -0.06437 0.1255
## m3 1.553 -0.05148 0.0155
## m1 1.553 7.568e-05 -0.05185
## m6 1.577 -0.05503
## lst_wet df logLik AICc delta weight
## m4 4 32.702 -55.8 0.00 0.231
## m5 4 32.568 -55.5 0.27 0.202
## m2 4 32.437 -55.3 0.53 0.177
## m3 4 32.130 -54.7 1.14 0.131
## m1 4 32.130 -54.7 1.14 0.130
## m6 -8.377e-05 4 32.114 -54.6 1.18 0.128
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m4 1.665 -0.07614 0.001014
## m5 1.485 -0.04104 0.001523
## m2 1.608 -0.06437 0.1255
## m3 1.553 -0.05148 0.0155
## m1 1.553 7.568e-05 -0.05185
## m6 1.577 -0.05503
## lst_wet df logLik AICc delta weight
## m4 4 32.702 -55.8 0.00 0.231
## m5 4 32.568 -55.5 0.27 0.202
## m2 4 32.437 -55.3 0.53 0.177
## m3 4 32.130 -54.7 1.14 0.131
## m1 4 32.130 -54.7 1.14 0.130
## m6 -8.377e-05 4 32.114 -54.6 1.18 0.128
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m4 1.665 -0.07614 0.001014
## m5 1.485 -0.04104 0.001523
## m2 1.608 -0.06437 0.1255
## m3 1.553 -0.05148 0.0155
## m1 1.553 7.568e-05 -0.05185
## m6 1.577 -0.05503
## lst_wet df logLik AICc delta weight
## m4 4 32.702 -55.8 0.00 0.231
## m5 4 32.568 -55.5 0.27 0.202
## m2 4 32.437 -55.3 0.53 0.177
## m3 4 32.130 -54.7 1.14 0.131
## m1 4 32.130 -54.7 1.14 0.130
## m6 -8.377e-05 4 32.114 -54.6 1.18 0.128
## Models ranked by AICc(x)
## Warning in min(x): no non-missing arguments to min; returning Inf
## Warning in max(x): no non-missing arguments to max; returning -Inf
## Warning in matrix(value, n, p): data length [11] is not a sub-multiple or
## multiple of the number of columns [10]
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m2 2.561 -0.1228 -0.758
## m6 2.746 -0.1623
## m4 2.679 -0.1364 -0.001112
## m5 2.578 -0.1313 -0.003044
## m3 2.603 -0.1353 -0.06928
## m1 2.639 1.472e-05 -0.1411
## lst_wet df logLik AICc delta weight
## m2 4 20.724 -31.8 0.00 0.265
## m6 0.001337 4 20.522 -31.4 0.40 0.216
## m4 4 20.278 -30.9 0.89 0.169
## m5 4 20.053 -30.4 1.34 0.135
## m3 4 19.866 -30.1 1.72 0.112
## m1 4 19.768 -29.9 1.91 0.102
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m2 2.561 -0.1228 -0.758
## m6 2.746 -0.1623
## m4 2.679 -0.1364 -0.001112
## m5 2.578 -0.1313 -0.003044
## m3 2.603 -0.1353 -0.06928
## m1 2.639 1.472e-05 -0.1411
## lst_wet df logLik AICc delta weight
## m2 4 20.724 -31.8 0.00 0.265
## m6 0.001337 4 20.522 -31.4 0.40 0.216
## m4 4 20.278 -30.9 0.89 0.169
## m5 4 20.053 -30.4 1.34 0.135
## m3 4 19.866 -30.1 1.72 0.112
## m1 4 19.768 -29.9 1.91 0.102
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m2 2.561 -0.1228 -0.758
## m6 2.746 -0.1623
## m4 2.679 -0.1364 -0.001112
## m5 2.578 -0.1313 -0.003044
## m3 2.603 -0.1353 -0.06928
## m1 2.639 1.472e-05 -0.1411
## lst_wet df logLik AICc delta weight
## m2 4 20.724 -31.8 0.00 0.265
## m6 0.001337 4 20.522 -31.4 0.40 0.216
## m4 4 20.278 -30.9 0.89 0.169
## m5 4 20.053 -30.4 1.34 0.135
## m3 4 19.866 -30.1 1.72 0.112
## m1 4 19.768 -29.9 1.91 0.102
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m2 2.561 -0.1228 -0.758
## m6 2.746 -0.1623
## m4 2.679 -0.1364 -0.001112
## m5 2.578 -0.1313 -0.003044
## m3 2.603 -0.1353 -0.06928
## m1 2.639 1.472e-05 -0.1411
## lst_wet df logLik AICc delta weight
## m2 4 20.724 -31.8 0.00 0.265
## m6 0.001337 4 20.522 -31.4 0.40 0.216
## m4 4 20.278 -30.9 0.89 0.169
## m5 4 20.053 -30.4 1.34 0.135
## m3 4 19.866 -30.1 1.72 0.112
## m1 4 19.768 -29.9 1.91 0.102
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m2 2.561 -0.1228 -0.758
## m6 2.746 -0.1623
## m4 2.679 -0.1364 -0.001112
## m5 2.578 -0.1313 -0.003044
## m3 2.603 -0.1353 -0.06928
## m1 2.639 1.472e-05 -0.1411
## lst_wet df logLik AICc delta weight
## m2 4 20.724 -31.8 0.00 0.265
## m6 0.001337 4 20.522 -31.4 0.40 0.216
## m4 4 20.278 -30.9 0.89 0.169
## m5 4 20.053 -30.4 1.34 0.135
## m3 4 19.866 -30.1 1.72 0.112
## m1 4 19.768 -29.9 1.91 0.102
## Models ranked by AICc(x)
## Warning in min(x): no non-missing arguments to min; returning Inf
## Warning in max(x): no non-missing arguments to max; returning -Inf
## Warning in matrix(value, n, p): data length [11] is not a sub-multiple or
## multiple of the number of columns [10]
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m4 0.7899 0.10620 -0.001544
## m5 1.0290 0.04865 -0.003963
## m3 1.0150 0.05103 -0.1236
## m2 0.9789 0.05694 0.005968
## m6 0.9805 0.05702
## m1 0.9817 -3.174e-05 0.05683
## lst_wet df logLik AICc delta weight
## m4 4 43.063 -76.5 0.00 0.377
## m5 4 42.180 -74.8 1.77 0.156
## m3 4 41.985 -74.4 2.16 0.128
## m2 4 41.855 -74.1 2.42 0.113
## m6 -0.0001047 4 41.854 -74.1 2.42 0.113
## m1 4 41.852 -74.1 2.42 0.112
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m4 0.7899 0.10620 -0.001544
## m5 1.0290 0.04865 -0.003963
## m3 1.0150 0.05103 -0.1236
## m2 0.9789 0.05694 0.005968
## m6 0.9805 0.05702
## m1 0.9817 -3.174e-05 0.05683
## lst_wet df logLik AICc delta weight
## m4 4 43.063 -76.5 0.00 0.377
## m5 4 42.180 -74.8 1.77 0.156
## m3 4 41.985 -74.4 2.16 0.128
## m2 4 41.855 -74.1 2.42 0.113
## m6 -0.0001047 4 41.854 -74.1 2.42 0.113
## m1 4 41.852 -74.1 2.42 0.112
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m4 0.7899 0.10620 -0.001544
## m5 1.0290 0.04865 -0.003963
## m3 1.0150 0.05103 -0.1236
## m2 0.9789 0.05694 0.005968
## m6 0.9805 0.05702
## m1 0.9817 -3.174e-05 0.05683
## lst_wet df logLik AICc delta weight
## m4 4 43.063 -76.5 0.00 0.377
## m5 4 42.180 -74.8 1.77 0.156
## m3 4 41.985 -74.4 2.16 0.128
## m2 4 41.855 -74.1 2.42 0.113
## m6 -0.0001047 4 41.854 -74.1 2.42 0.113
## m1 4 41.852 -74.1 2.42 0.112
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m4 0.7899 0.10620 -0.001544
## m5 1.0290 0.04865 -0.003963
## m3 1.0150 0.05103 -0.1236
## m2 0.9789 0.05694 0.005968
## m6 0.9805 0.05702
## m1 0.9817 -3.174e-05 0.05683
## lst_wet df logLik AICc delta weight
## m4 4 43.063 -76.5 0.00 0.377
## m5 4 42.180 -74.8 1.77 0.156
## m3 4 41.985 -74.4 2.16 0.128
## m2 4 41.855 -74.1 2.42 0.113
## m6 -0.0001047 4 41.854 -74.1 2.42 0.113
## m1 4 41.852 -74.1 2.42 0.112
## Models ranked by AICc(x)
## Warning in min(x): no non-missing arguments to min; returning Inf
## Warning in max(x): no non-missing arguments to max; returning -Inf
## Warning in matrix(value, n, p): data length [11] is not a sub-multiple or
## multiple of the number of columns [10]
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m5 0.3382 0.18040 0.03945
## m4 0.4091 0.19830 -0.005406
## m3 0.2763 0.19480 0.2222
## m1 0.2773 0.001783 0.18280
## m6 0.8450 0.09020
## m2 0.8621 0.08973 0.01891
## lst_wet df logLik AICc delta weight
## m5 4 32.128 -54.7 0.00 0.547
## m4 4 30.844 -52.1 2.57 0.152
## m3 4 30.805 -52.0 2.65 0.146
## m1 4 30.783 -52.0 2.69 0.143
## m6 0.001179 4 27.933 -46.3 8.39 0.008
## m2 4 27.358 -45.1 9.54 0.005
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m5 0.3382 0.18040 0.03945
## m4 0.4091 0.19830 -0.005406
## m3 0.2763 0.19480 0.2222
## m1 0.2773 0.001783 0.18280
## m6 0.8450 0.09020
## m2 0.8621 0.08973 0.01891
## lst_wet df logLik AICc delta weight
## m5 4 32.128 -54.7 0.00 0.547
## m4 4 30.844 -52.1 2.57 0.152
## m3 4 30.805 -52.0 2.65 0.146
## m1 4 30.783 -52.0 2.69 0.143
## m6 0.001179 4 27.933 -46.3 8.39 0.008
## m2 4 27.358 -45.1 9.54 0.005
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m5 0.3382 0.18040 0.03945
## m4 0.4091 0.19830 -0.005406
## m3 0.2763 0.19480 0.2222
## m1 0.2773 0.001783 0.18280
## m6 0.8450 0.09020
## m2 0.8621 0.08973 0.01891
## lst_wet df logLik AICc delta weight
## m5 4 32.128 -54.7 0.00 0.547
## m4 4 30.844 -52.1 2.57 0.152
## m3 4 30.805 -52.0 2.65 0.146
## m1 4 30.783 -52.0 2.69 0.143
## m6 0.001179 4 27.933 -46.3 8.39 0.008
## m2 4 27.358 -45.1 9.54 0.005
## Models ranked by AICc(x)
## Warning in matrix(value, n, p): data length [11] is not a sub-multiple or
## multiple of the number of columns [10]
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m1 1.368 0.0006329 -0.01786
## m5 1.414 -0.02236 0.00523
## m4 1.422 -0.02080 -0.0003044
## m2 1.432 -0.02486 -0.02072
## m3 1.433 -0.02531 -0.008356
## m6 1.432 -0.02508
## lst_wet df logLik AICc delta weight
## m1 4 24.350 -39.1 0.00 0.182
## m5 4 24.265 -38.9 0.17 0.167
## m4 4 24.258 -38.9 0.18 0.166
## m2 4 24.237 -38.9 0.23 0.162
## m3 4 24.236 -38.9 0.23 0.162
## m6 -1.482e-05 4 24.235 -38.9 0.23 0.162
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m1 1.368 0.0006329 -0.01786
## m5 1.414 -0.02236 0.00523
## m4 1.422 -0.02080 -0.0003044
## m2 1.432 -0.02486 -0.02072
## m3 1.433 -0.02531 -0.008356
## m6 1.432 -0.02508
## lst_wet df logLik AICc delta weight
## m1 4 24.350 -39.1 0.00 0.182
## m5 4 24.265 -38.9 0.17 0.167
## m4 4 24.258 -38.9 0.18 0.166
## m2 4 24.237 -38.9 0.23 0.162
## m3 4 24.236 -38.9 0.23 0.162
## m6 -1.482e-05 4 24.235 -38.9 0.23 0.162
## Models ranked by AICc(x)
## Warning in min(x): no non-missing arguments to min; returning Inf
## Warning in max(x): no non-missing arguments to max; returning -Inf
## Warning in matrix(value, n, p): data length [11] is not a sub-multiple or
## multiple of the number of columns [10]
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m3 1.632 -0.010110 -0.2118
## m4 1.585 -0.028100 0.002776
## m5 1.501 0.010940 -0.007824
## m2 1.310 0.026660 0.4561
## m1 1.559 -0.0002744 -0.002724
## m6 1.249 0.054410
## lst_wet df logLik AICc delta weight
## m3 4 31.324 -53.0 0.00 0.572
## m4 4 30.566 -51.5 1.52 0.268
## m5 4 29.500 -49.4 3.65 0.092
## m2 4 28.389 -47.2 5.87 0.030
## m1 4 28.076 -46.6 6.50 0.022
## m6 -0.001912 4 27.697 -45.8 7.25 0.015
## Models ranked by AICc(x)
## Warning in min(x): no non-missing arguments to min; returning Inf
## Warning in max(x): no non-missing arguments to max; returning -Inf
## Warning in matrix(value, n, p): data length [11] is not a sub-multiple or
## multiple of the number of columns [10]
## Error in nrow(concord.out): object 'concord.out' not found
mininit<-filter(concord.out9c, Sites==1)%>%filter(Model=="rain")%>%select(Reference, Target, Kendall)
## Error in filter_(.data, .dots = lazyeval::lazy_dots(...)): object 'concord.out9c' not found
mininit$tribe<-as.factor(c("X", "T", "T", "T", "X", "X", "X","X","X", "B", "B", "T", "T", "X","X", "T", "X", "X", "X", "X", "X"))
## Error in mininit$tribe <- as.factor(c("X", "T", "T", "T", "X", "X", "X", : object 'mininit' not found
tapply(mininit$Kendall, mininit$tribe, mean)
## Error in tapply(mininit$Kendall, mininit$tribe, mean): object 'mininit' not found
#tribe isnt as good a magic bullet as i thought....
sites<-c("puertorico", "macae", "frenchguiana", "costarica", "colombia", "argentina")
concord.out10<-concord.magic(sites, "sqrt.decomp", fulldata, 10, 2, "gaussian")#rain models hardly ever converge
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m4 0.8745 -0.032580 0.01795 -0.10630
## m0 0.9199 -0.046160
## m1 0.8396 -0.031810 0.014380
## m2 0.8896 -0.040980 0.01751
## m3 0.8692 -0.036220 0.009524 -0.004328
## m9 0.8131 -0.027270 0.014620 0.01957
## m10 0.7396 -0.008275 0.026300 0.02043 -0.11650
## m11 0.8177 -0.027460 0.010420 0.03219 -0.003906
## m12 0.5549 0.021030 0.048330 0.03615 0.016770 -0.06886
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m4
## m0
## m1
## m2
## m3
## m9 0.005475
## m10 0.004869 -0.03343
## m11 -0.009356
## m12 -0.013000 -0.10280
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik AICc
## m4 5 43.963 -75.4
## m0 3 41.041 -75.2
## m1 4 41.931 -74.3
## m2 4 41.428 -73.3
## m3 5 42.017 -71.5
## m9 6 42.425 -69.2
## m10 8 45.530 -68.2
## m11 -0.01386 8 42.812 -62.8
## m12 -0.01581 -0.05728 11 46.905 -57.1
## delta weight
## m4 0.00 0.335
## m0 0.27 0.293
## m1 1.16 0.187
## m2 2.17 0.113
## m3 3.89 0.048
## m9 6.23 0.015
## m10 7.22 0.009
## m11 12.66 0.001
## m12 18.28 0.000
## Models ranked by AICc(x)
## Warning in matrix(value, n, p): data length [11] is not a sub-multiple or
## multiple of the number of columns [10]
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m4 0.8745 -0.032580 0.01795 -0.10630
## m0 0.9199 -0.046160
## m1 0.8396 -0.031810 0.014380
## m2 0.8896 -0.040980 0.01751
## m3 0.8692 -0.036220 0.009524 -0.004328
## m9 0.8131 -0.027270 0.014620 0.01957
## m10 0.7396 -0.008275 0.026300 0.02043 -0.11650
## m11 0.8177 -0.027460 0.010420 0.03219 -0.003906
## m12 0.5549 0.021030 0.048330 0.03615 0.016770 -0.06886
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m4
## m0
## m1
## m2
## m3
## m9 0.005475
## m10 0.004869 -0.03343
## m11 -0.009356
## m12 -0.013000 -0.10280
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik AICc
## m4 5 43.963 -75.4
## m0 3 41.041 -75.2
## m1 4 41.931 -74.3
## m2 4 41.428 -73.3
## m3 5 42.017 -71.5
## m9 6 42.425 -69.2
## m10 8 45.530 -68.2
## m11 -0.01386 8 42.812 -62.8
## m12 -0.01581 -0.05728 11 46.905 -57.1
## delta weight
## m4 0.00 0.335
## m0 0.27 0.293
## m1 1.16 0.187
## m2 2.17 0.113
## m3 3.89 0.048
## m9 6.23 0.015
## m10 7.22 0.009
## m11 12.66 0.001
## m12 18.28 0.000
## Models ranked by AICc(x)
## Warning in matrix(value, n, p): data length [11] is not a sub-multiple or
## multiple of the number of columns [10]
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m4 0.8745 -0.032580 0.01795 -0.10630
## m0 0.9199 -0.046160
## m1 0.8396 -0.031810 0.014380
## m2 0.8896 -0.040980 0.01751
## m3 0.8692 -0.036220 0.009524 -0.004328
## m9 0.8131 -0.027270 0.014620 0.01957
## m10 0.7396 -0.008275 0.026300 0.02043 -0.11650
## m11 0.8177 -0.027460 0.010420 0.03219 -0.003906
## m12 0.5549 0.021030 0.048330 0.03615 0.016770 -0.06886
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m4
## m0
## m1
## m2
## m3
## m9 0.005475
## m10 0.004869 -0.03343
## m11 -0.009356
## m12 -0.013000 -0.10280
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik AICc
## m4 5 43.963 -75.4
## m0 3 41.041 -75.2
## m1 4 41.931 -74.3
## m2 4 41.428 -73.3
## m3 5 42.017 -71.5
## m9 6 42.425 -69.2
## m10 8 45.530 -68.2
## m11 -0.01386 8 42.812 -62.8
## m12 -0.01581 -0.05728 11 46.905 -57.1
## delta weight
## m4 0.00 0.335
## m0 0.27 0.293
## m1 1.16 0.187
## m2 2.17 0.113
## m3 3.89 0.048
## m9 6.23 0.015
## m10 7.22 0.009
## m11 12.66 0.001
## m12 18.28 0.000
## Models ranked by AICc(x)
## Warning in matrix(value, n, p): data length [11] is not a sub-multiple or
## multiple of the number of columns [10]
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m4 0.8745 -0.032580 0.01795 -0.10630
## m0 0.9199 -0.046160
## m1 0.8396 -0.031810 0.014380
## m2 0.8896 -0.040980 0.01751
## m3 0.8692 -0.036220 0.009524 -0.004328
## m9 0.8131 -0.027270 0.014620 0.01957
## m10 0.7396 -0.008275 0.026300 0.02043 -0.11650
## m11 0.8177 -0.027460 0.010420 0.03219 -0.003906
## m12 0.5549 0.021030 0.048330 0.03615 0.016770 -0.06886
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m4
## m0
## m1
## m2
## m3
## m9 0.005475
## m10 0.004869 -0.03343
## m11 -0.009356
## m12 -0.013000 -0.10280
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik AICc
## m4 5 43.963 -75.4
## m0 3 41.041 -75.2
## m1 4 41.931 -74.3
## m2 4 41.428 -73.3
## m3 5 42.017 -71.5
## m9 6 42.425 -69.2
## m10 8 45.530 -68.2
## m11 -0.01386 8 42.812 -62.8
## m12 -0.01581 -0.05728 11 46.905 -57.1
## delta weight
## m4 0.00 0.335
## m0 0.27 0.293
## m1 1.16 0.187
## m2 2.17 0.113
## m3 3.89 0.048
## m9 6.23 0.015
## m10 7.22 0.009
## m11 12.66 0.001
## m12 18.28 0.000
## Models ranked by AICc(x)
## Warning in matrix(value, n, p): data length [11] is not a sub-multiple or
## multiple of the number of columns [10]
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m4 0.8745 -0.032580 0.01795 -0.10630
## m0 0.9199 -0.046160
## m1 0.8396 -0.031810 0.014380
## m2 0.8896 -0.040980 0.01751
## m3 0.8692 -0.036220 0.009524 -0.004328
## m9 0.8131 -0.027270 0.014620 0.01957
## m10 0.7396 -0.008275 0.026300 0.02043 -0.11650
## m11 0.8177 -0.027460 0.010420 0.03219 -0.003906
## m12 0.5549 0.021030 0.048330 0.03615 0.016770 -0.06886
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m4
## m0
## m1
## m2
## m3
## m9 0.005475
## m10 0.004869 -0.03343
## m11 -0.009356
## m12 -0.013000 -0.10280
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik AICc
## m4 5 43.963 -75.4
## m0 3 41.041 -75.2
## m1 4 41.931 -74.3
## m2 4 41.428 -73.3
## m3 5 42.017 -71.5
## m9 6 42.425 -69.2
## m10 8 45.530 -68.2
## m11 -0.01386 8 42.812 -62.8
## m12 -0.01581 -0.05728 11 46.905 -57.1
## delta weight
## m4 0.00 0.335
## m0 0.27 0.293
## m1 1.16 0.187
## m2 2.17 0.113
## m3 3.89 0.048
## m9 6.23 0.015
## m10 7.22 0.009
## m11 12.66 0.001
## m12 18.28 0.000
## Models ranked by AICc(x)
## Warning in matrix(value, n, p): data length [11] is not a sub-multiple or
## multiple of the number of columns [10]
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 1.298 -0.07487
## m1 1.500 -0.10360 0.016990
## m2 1.477 -0.10080 0.01787
## m3 1.655 -0.12410 0.002056 -0.014310
## m4 1.147 -0.05613 0.01539 0.06718
## m9 1.579 -0.11490 0.017200 0.01179
## m11 1.610 -0.11770 0.002038 -0.01071 -0.014210
## m10 1.245 -0.06930 0.026260 0.00904 0.05769
## m12 1.009 -0.03507 0.023610 -0.01742 -0.001891 0.09425
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m1
## m2
## m3
## m4
## m9 -0.0280300
## m11 -0.0013460
## m10 -0.0289300 -0.03092
## m12 -0.0009271 -0.06806
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik AICc
## m0 3 36.134 -65.3
## m1 4 37.049 -64.5
## m2 4 36.405 -63.2
## m3 5 37.762 -63.0
## m4 5 37.024 -61.5
## m9 6 38.280 -60.9
## m11 0.02487 8 39.871 -56.9
## m10 8 39.127 -55.4
## m12 0.02678 -0.03466 11 41.043 -45.4
## delta weight
## m0 0.00 0.386
## m1 0.85 0.253
## m2 2.14 0.133
## m3 2.32 0.121
## m4 3.80 0.058
## m9 4.44 0.042
## m11 8.46 0.006
## m10 9.95 0.003
## m12 19.93 0.000
## Models ranked by AICc(x)
## Warning in matrix(value, n, p): data length [11] is not a sub-multiple or
## multiple of the number of columns [10]
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 1.298 -0.07487
## m1 1.500 -0.10360 0.016990
## m2 1.477 -0.10080 0.01787
## m3 1.655 -0.12410 0.002056 -0.014310
## m4 1.147 -0.05613 0.01539 0.06718
## m9 1.579 -0.11490 0.017200 0.01179
## m11 1.610 -0.11770 0.002038 -0.01071 -0.014210
## m10 1.245 -0.06930 0.026260 0.00904 0.05769
## m12 1.009 -0.03507 0.023610 -0.01742 -0.001891 0.09425
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m1
## m2
## m3
## m4
## m9 -0.0280300
## m11 -0.0013460
## m10 -0.0289300 -0.03092
## m12 -0.0009271 -0.06806
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik AICc
## m0 3 36.134 -65.3
## m1 4 37.049 -64.5
## m2 4 36.405 -63.2
## m3 5 37.762 -63.0
## m4 5 37.024 -61.5
## m9 6 38.280 -60.9
## m11 0.02487 8 39.871 -56.9
## m10 8 39.127 -55.4
## m12 0.02678 -0.03466 11 41.043 -45.4
## delta weight
## m0 0.00 0.386
## m1 0.85 0.253
## m2 2.14 0.133
## m3 2.32 0.121
## m4 3.80 0.058
## m9 4.44 0.042
## m11 8.46 0.006
## m10 9.95 0.003
## m12 19.93 0.000
## Models ranked by AICc(x)
## Warning in matrix(value, n, p): data length [11] is not a sub-multiple or
## multiple of the number of columns [10]
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 1.298 -0.07487
## m1 1.500 -0.10360 0.016990
## m2 1.477 -0.10080 0.01787
## m3 1.655 -0.12410 0.002056 -0.014310
## m4 1.147 -0.05613 0.01539 0.06718
## m9 1.579 -0.11490 0.017200 0.01179
## m11 1.610 -0.11770 0.002038 -0.01071 -0.014210
## m10 1.245 -0.06930 0.026260 0.00904 0.05769
## m12 1.009 -0.03507 0.023610 -0.01742 -0.001891 0.09425
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m1
## m2
## m3
## m4
## m9 -0.0280300
## m11 -0.0013460
## m10 -0.0289300 -0.03092
## m12 -0.0009271 -0.06806
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik AICc
## m0 3 36.134 -65.3
## m1 4 37.049 -64.5
## m2 4 36.405 -63.2
## m3 5 37.762 -63.0
## m4 5 37.024 -61.5
## m9 6 38.280 -60.9
## m11 0.02487 8 39.871 -56.9
## m10 8 39.127 -55.4
## m12 0.02678 -0.03466 11 41.043 -45.4
## delta weight
## m0 0.00 0.386
## m1 0.85 0.253
## m2 2.14 0.133
## m3 2.32 0.121
## m4 3.80 0.058
## m9 4.44 0.042
## m11 8.46 0.006
## m10 9.95 0.003
## m12 19.93 0.000
## Models ranked by AICc(x)
## Warning in matrix(value, n, p): data length [11] is not a sub-multiple or
## multiple of the number of columns [10]
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 1.298 -0.07487
## m1 1.500 -0.10360 0.016990
## m2 1.477 -0.10080 0.01787
## m3 1.655 -0.12410 0.002056 -0.014310
## m4 1.147 -0.05613 0.01539 0.06718
## m9 1.579 -0.11490 0.017200 0.01179
## m11 1.610 -0.11770 0.002038 -0.01071 -0.014210
## m10 1.245 -0.06930 0.026260 0.00904 0.05769
## m12 1.009 -0.03507 0.023610 -0.01742 -0.001891 0.09425
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m1
## m2
## m3
## m4
## m9 -0.0280300
## m11 -0.0013460
## m10 -0.0289300 -0.03092
## m12 -0.0009271 -0.06806
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik AICc
## m0 3 36.134 -65.3
## m1 4 37.049 -64.5
## m2 4 36.405 -63.2
## m3 5 37.762 -63.0
## m4 5 37.024 -61.5
## m9 6 38.280 -60.9
## m11 0.02487 8 39.871 -56.9
## m10 8 39.127 -55.4
## m12 0.02678 -0.03466 11 41.043 -45.4
## delta weight
## m0 0.00 0.386
## m1 0.85 0.253
## m2 2.14 0.133
## m3 2.32 0.121
## m4 3.80 0.058
## m9 4.44 0.042
## m11 8.46 0.006
## m10 9.95 0.003
## m12 19.93 0.000
## Models ranked by AICc(x)
## Warning in matrix(value, n, p): data length [11] is not a sub-multiple or
## multiple of the number of columns [10]
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m9 -0.149600 0.07256 -1.465e-02 0.02828
## m2 -0.081620 0.06067 0.03370
## m4 -0.165400 0.07250 0.03576 0.06159
## m0 0.061550 0.03422
## m1 0.020440 0.04120 -1.362e-02
## m11 -0.186200 0.07700 -4.687e-05 0.01805 0.013770
## m10 -0.294300 0.09480 -2.663e-02 0.03205 0.07532
## m3 -0.004089 0.04347 6.791e-04 0.013420
## m12 -0.349200 0.10340 -1.903e-02 0.02196 0.008067 0.06088
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m9 -0.03058
## m2
## m4
## m0
## m1
## m11 -0.01776
## m10 -0.03098 0.03514
## m3
## m12 -0.01757 0.05747
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik AICc
## m9 6 50.442 -85.2
## m2 4 46.763 -83.9
## m4 5 47.880 -83.3
## m0 3 45.047 -83.2
## m1 4 46.134 -82.7
## m11 0.01205 8 52.584 -82.3
## m10 8 52.578 -82.3
## m3 5 47.326 -82.2
## m12 0.01268 0.01865 11 55.402 -74.1
## delta weight
## m9 0.00 0.312
## m2 1.31 0.162
## m4 1.97 0.116
## m0 2.06 0.111
## m1 2.56 0.086
## m11 2.92 0.072
## m10 2.93 0.072
## m3 3.08 0.067
## m12 11.09 0.001
## Models ranked by AICc(x)
## Warning in matrix(value, n, p): data length [11] is not a sub-multiple or
## multiple of the number of columns [10]
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m9 -0.149600 0.07256 -1.465e-02 0.02828
## m2 -0.081620 0.06067 0.03370
## m4 -0.165400 0.07250 0.03576 0.06159
## m0 0.061550 0.03422
## m1 0.020440 0.04120 -1.362e-02
## m11 -0.186200 0.07700 -4.687e-05 0.01805 0.013770
## m10 -0.294300 0.09480 -2.663e-02 0.03205 0.07532
## m3 -0.004089 0.04347 6.791e-04 0.013420
## m12 -0.349200 0.10340 -1.903e-02 0.02196 0.008067 0.06088
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m9 -0.03058
## m2
## m4
## m0
## m1
## m11 -0.01776
## m10 -0.03098 0.03514
## m3
## m12 -0.01757 0.05747
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik AICc
## m9 6 50.442 -85.2
## m2 4 46.763 -83.9
## m4 5 47.880 -83.3
## m0 3 45.047 -83.2
## m1 4 46.134 -82.7
## m11 0.01205 8 52.584 -82.3
## m10 8 52.578 -82.3
## m3 5 47.326 -82.2
## m12 0.01268 0.01865 11 55.402 -74.1
## delta weight
## m9 0.00 0.312
## m2 1.31 0.162
## m4 1.97 0.116
## m0 2.06 0.111
## m1 2.56 0.086
## m11 2.92 0.072
## m10 2.93 0.072
## m3 3.08 0.067
## m12 11.09 0.001
## Models ranked by AICc(x)
## Warning in matrix(value, n, p): data length [11] is not a sub-multiple or
## multiple of the number of columns [10]
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m9 -0.149600 0.07256 -1.465e-02 0.02828
## m2 -0.081620 0.06067 0.03370
## m4 -0.165400 0.07250 0.03576 0.06159
## m0 0.061550 0.03422
## m1 0.020440 0.04120 -1.362e-02
## m11 -0.186200 0.07700 -4.687e-05 0.01805 0.013770
## m10 -0.294300 0.09480 -2.663e-02 0.03205 0.07532
## m3 -0.004089 0.04347 6.791e-04 0.013420
## m12 -0.349200 0.10340 -1.903e-02 0.02196 0.008067 0.06088
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m9 -0.03058
## m2
## m4
## m0
## m1
## m11 -0.01776
## m10 -0.03098 0.03514
## m3
## m12 -0.01757 0.05747
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik AICc
## m9 6 50.442 -85.2
## m2 4 46.763 -83.9
## m4 5 47.880 -83.3
## m0 3 45.047 -83.2
## m1 4 46.134 -82.7
## m11 0.01205 8 52.584 -82.3
## m10 8 52.578 -82.3
## m3 5 47.326 -82.2
## m12 0.01268 0.01865 11 55.402 -74.1
## delta weight
## m9 0.00 0.312
## m2 1.31 0.162
## m4 1.97 0.116
## m0 2.06 0.111
## m1 2.56 0.086
## m11 2.92 0.072
## m10 2.93 0.072
## m3 3.08 0.067
## m12 11.09 0.001
## Models ranked by AICc(x)
## Warning in matrix(value, n, p): data length [11] is not a sub-multiple or
## multiple of the number of columns [10]
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 -0.193300 0.1540
## m2 -0.200700 0.1554 0.02612
## m1 -0.164000 0.1478 -0.011240
## m4 -0.203900 0.1529 0.02610 0.05073
## m9 -0.009772 0.1178 -0.011800 0.01994
## m3 -0.158800 0.1472 -0.013820 -0.002400
## m10 -0.102800 0.1336 0.006983 0.02056 0.03730
## m11 -0.005622 0.1175 -0.014630 0.02038 -0.002638
## m12 -0.092480 0.1312 0.008892 0.02187 0.001948 0.05022
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m2
## m1
## m4
## m9 -0.02426
## m3
## m10 -0.02213 -0.05772
## m11 -0.02484
## m12 -0.02410 -0.07227
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik AICc
## m0 3 45.010 -83.1
## m2 4 46.179 -82.8
## m1 4 45.744 -81.9
## m4 5 46.931 -81.4
## m9 6 48.022 -80.4
## m3 5 45.780 -79.1
## m10 8 50.173 -77.5
## m11 -0.0004993 8 48.073 -73.3
## m12 -0.0015380 -0.01397 11 50.316 -64.0
## delta weight
## m0 0.00 0.306
## m2 0.34 0.258
## m1 1.21 0.167
## m4 1.73 0.128
## m9 2.70 0.079
## m3 4.04 0.041
## m10 5.61 0.019
## m11 9.81 0.002
## m12 19.13 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 -0.193300 0.1540
## m2 -0.200700 0.1554 0.02612
## m1 -0.164000 0.1478 -0.011240
## m4 -0.203900 0.1529 0.02610 0.05073
## m9 -0.009772 0.1178 -0.011800 0.01994
## m3 -0.158800 0.1472 -0.013820 -0.002400
## m10 -0.102800 0.1336 0.006983 0.02056 0.03730
## m11 -0.005622 0.1175 -0.014630 0.02038 -0.002638
## m12 -0.092480 0.1312 0.008892 0.02187 0.001948 0.05022
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m2
## m1
## m4
## m9 -0.02426
## m3
## m10 -0.02213 -0.05772
## m11 -0.02484
## m12 -0.02410 -0.07227
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik AICc
## m0 3 45.010 -83.1
## m2 4 46.179 -82.8
## m1 4 45.744 -81.9
## m4 5 46.931 -81.4
## m9 6 48.022 -80.4
## m3 5 45.780 -79.1
## m10 8 50.173 -77.5
## m11 -0.0004993 8 48.073 -73.3
## m12 -0.0015380 -0.01397 11 50.316 -64.0
## delta weight
## m0 0.00 0.306
## m2 0.34 0.258
## m1 1.21 0.167
## m4 1.73 0.128
## m9 2.70 0.079
## m3 4.04 0.041
## m10 5.61 0.019
## m11 9.81 0.002
## m12 19.13 0.000
## Models ranked by AICc(x)
## Warning in matrix(value, n, p): data length [11] is not a sub-multiple or
## multiple of the number of columns [10]
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 0.4386 0.012730
## m3 0.4509 0.013510 -0.024510 -0.019410
## m2 0.4822 0.005232 -0.02142
## m1 0.4401 0.012310 -0.003769
## m4 0.4794 0.005966 -0.02108 -0.004459
## m9 0.4880 0.004056 -0.004115 -0.01522
## m11 0.4965 0.005623 -0.024650 -0.01326 -0.019240
## m10 0.4724 0.006518 -0.016190 -0.01411 0.004228
## m12 0.4551 0.011130 -0.025650 -0.01026 -0.008652 0.029790
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m3
## m2
## m1
## m4
## m9 0.02751
## m11 0.02600
## m10 0.02745 0.038020
## m12 0.02528 0.003417
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik AICc
## m0 3 43.727 -80.5
## m3 5 46.009 -79.5
## m2 4 44.322 -79.0
## m1 4 43.802 -78.0
## m4 5 44.326 -76.2
## m9 6 45.811 -76.0
## m11 -0.001369 8 48.316 -73.8
## m10 8 46.262 -69.7
## m12 -0.001935 -0.03344 11 49.301 -61.9
## delta weight
## m0 0.00 0.383
## m3 1.01 0.231
## m2 1.49 0.182
## m1 2.53 0.108
## m4 4.38 0.043
## m9 4.56 0.039
## m11 6.76 0.013
## m10 10.86 0.002
## m12 18.59 0.000
## Models ranked by AICc(x)
## Warning in matrix(value, n, p): data length [11] is not a sub-multiple or
## multiple of the number of columns [10]
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m3 1.1380 -0.07813 -0.1827
## m1 1.1260 -0.0008689 -0.07304
## m5 1.1160 -0.07602 -0.003458
## m6 1.0930 -0.07008
## m4 1.0380 -0.07383 0.001278
## m2 0.9608 -0.05758 0.1407
## lst_wet df logLik AICc delta weight
## m3 4 46.823 -84.0 0.00 0.405
## m1 4 46.291 -83.0 1.06 0.238
## m5 4 45.977 -82.4 1.69 0.174
## m6 -0.002385 4 45.970 -82.3 1.71 0.173
## m4 4 42.811 -76.0 8.02 0.007
## m2 4 41.794 -74.0 10.06 0.003
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m3 1.1380 -0.07813 -0.1827
## m1 1.1260 -0.0008689 -0.07304
## m5 1.1160 -0.07602 -0.003458
## m6 1.0930 -0.07008
## m4 1.0380 -0.07383 0.001278
## m2 0.9608 -0.05758 0.1407
## lst_wet df logLik AICc delta weight
## m3 4 46.823 -84.0 0.00 0.405
## m1 4 46.291 -83.0 1.06 0.238
## m5 4 45.977 -82.4 1.69 0.174
## m6 -0.002385 4 45.970 -82.3 1.71 0.173
## m4 4 42.811 -76.0 8.02 0.007
## m2 4 41.794 -74.0 10.06 0.003
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m3 1.1380 -0.07813 -0.1827
## m1 1.1260 -0.0008689 -0.07304
## m5 1.1160 -0.07602 -0.003458
## m6 1.0930 -0.07008
## m4 1.0380 -0.07383 0.001278
## m2 0.9608 -0.05758 0.1407
## lst_wet df logLik AICc delta weight
## m3 4 46.823 -84.0 0.00 0.405
## m1 4 46.291 -83.0 1.06 0.238
## m5 4 45.977 -82.4 1.69 0.174
## m6 -0.002385 4 45.970 -82.3 1.71 0.173
## m4 4 42.811 -76.0 8.02 0.007
## m2 4 41.794 -74.0 10.06 0.003
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m3 1.1380 -0.07813 -0.1827
## m1 1.1260 -0.0008689 -0.07304
## m5 1.1160 -0.07602 -0.003458
## m6 1.0930 -0.07008
## m4 1.0380 -0.07383 0.001278
## m2 0.9608 -0.05758 0.1407
## lst_wet df logLik AICc delta weight
## m3 4 46.823 -84.0 0.00 0.405
## m1 4 46.291 -83.0 1.06 0.238
## m5 4 45.977 -82.4 1.69 0.174
## m6 -0.002385 4 45.970 -82.3 1.71 0.173
## m4 4 42.811 -76.0 8.02 0.007
## m2 4 41.794 -74.0 10.06 0.003
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m3 1.1380 -0.07813 -0.1827
## m1 1.1260 -0.0008689 -0.07304
## m5 1.1160 -0.07602 -0.003458
## m6 1.0930 -0.07008
## m4 1.0380 -0.07383 0.001278
## m2 0.9608 -0.05758 0.1407
## lst_wet df logLik AICc delta weight
## m3 4 46.823 -84.0 0.00 0.405
## m1 4 46.291 -83.0 1.06 0.238
## m5 4 45.977 -82.4 1.69 0.174
## m6 -0.002385 4 45.970 -82.3 1.71 0.173
## m4 4 42.811 -76.0 8.02 0.007
## m2 4 41.794 -74.0 10.06 0.003
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m1 1.140 -0.0007857 -0.04770
## m5 1.216 -0.06209 -0.00372
## m3 1.219 -0.06249 -0.1423
## m6 1.227 -0.06041
## m4 1.256 -0.07751 0.0009353
## m2 1.345 -0.08577 0.4395
## lst_wet df logLik AICc delta weight
## m1 4 38.229 -66.9 0.00 0.347
## m5 4 37.386 -65.2 1.69 0.149
## m3 4 37.363 -65.1 1.73 0.146
## m6 -0.0009563 4 37.245 -64.9 1.97 0.130
## m4 4 37.180 -64.8 2.10 0.122
## m2 4 37.047 -64.5 2.36 0.106
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m1 1.140 -0.0007857 -0.04770
## m5 1.216 -0.06209 -0.00372
## m3 1.219 -0.06249 -0.1423
## m6 1.227 -0.06041
## m4 1.256 -0.07751 0.0009353
## m2 1.345 -0.08577 0.4395
## lst_wet df logLik AICc delta weight
## m1 4 38.229 -66.9 0.00 0.347
## m5 4 37.386 -65.2 1.69 0.149
## m3 4 37.363 -65.1 1.73 0.146
## m6 -0.0009563 4 37.245 -64.9 1.97 0.130
## m4 4 37.180 -64.8 2.10 0.122
## m2 4 37.047 -64.5 2.36 0.106
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m1 1.140 -0.0007857 -0.04770
## m5 1.216 -0.06209 -0.00372
## m3 1.219 -0.06249 -0.1423
## m6 1.227 -0.06041
## m4 1.256 -0.07751 0.0009353
## m2 1.345 -0.08577 0.4395
## lst_wet df logLik AICc delta weight
## m1 4 38.229 -66.9 0.00 0.347
## m5 4 37.386 -65.2 1.69 0.149
## m3 4 37.363 -65.1 1.73 0.146
## m6 -0.0009563 4 37.245 -64.9 1.97 0.130
## m4 4 37.180 -64.8 2.10 0.122
## m2 4 37.047 -64.5 2.36 0.106
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m1 1.140 -0.0007857 -0.04770
## m5 1.216 -0.06209 -0.00372
## m3 1.219 -0.06249 -0.1423
## m6 1.227 -0.06041
## m4 1.256 -0.07751 0.0009353
## m2 1.345 -0.08577 0.4395
## lst_wet df logLik AICc delta weight
## m1 4 38.229 -66.9 0.00 0.347
## m5 4 37.386 -65.2 1.69 0.149
## m3 4 37.363 -65.1 1.73 0.146
## m6 -0.0009563 4 37.245 -64.9 1.97 0.130
## m4 4 37.180 -64.8 2.10 0.122
## m2 4 37.047 -64.5 2.36 0.106
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m5 -0.02229 0.04850 0.006224
## m4 -0.09068 0.07382 -0.001262
## m3 0.01551 0.04216 0.1463
## m1 -0.01515 0.0004255 0.04638
## m6 0.07165 0.03257
## m2 0.06472 0.03334 0.006616
## lst_wet df logLik AICc delta weight
## m5 4 46.074 -82.5 0.00 0.281
## m4 4 46.042 -82.5 0.06 0.272
## m3 4 45.279 -81.0 1.59 0.127
## m1 4 45.204 -80.8 1.74 0.118
## m6 -0.000239 4 45.063 -80.5 2.02 0.102
## m2 4 45.052 -80.5 2.04 0.101
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m5 -0.02229 0.04850 0.006224
## m4 -0.09068 0.07382 -0.001262
## m3 0.01551 0.04216 0.1463
## m1 -0.01515 0.0004255 0.04638
## m6 0.07165 0.03257
## m2 0.06472 0.03334 0.006616
## lst_wet df logLik AICc delta weight
## m5 4 46.074 -82.5 0.00 0.281
## m4 4 46.042 -82.5 0.06 0.272
## m3 4 45.279 -81.0 1.59 0.127
## m1 4 45.204 -80.8 1.74 0.118
## m6 -0.000239 4 45.063 -80.5 2.02 0.102
## m2 4 45.052 -80.5 2.04 0.101
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m5 -0.02229 0.04850 0.006224
## m4 -0.09068 0.07382 -0.001262
## m3 0.01551 0.04216 0.1463
## m1 -0.01515 0.0004255 0.04638
## m6 0.07165 0.03257
## m2 0.06472 0.03334 0.006616
## lst_wet df logLik AICc delta weight
## m5 4 46.074 -82.5 0.00 0.281
## m4 4 46.042 -82.5 0.06 0.272
## m3 4 45.279 -81.0 1.59 0.127
## m1 4 45.204 -80.8 1.74 0.118
## m6 -0.000239 4 45.063 -80.5 2.02 0.102
## m2 4 45.052 -80.5 2.04 0.101
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m6 -0.2211 0.1551
## m5 -0.3170 0.1753 0.009211
## m2 -0.1753 0.1527 -0.05865
## m4 -0.2610 0.1700 -0.0007976
## m3 -0.1828 0.1521 -0.003951
## m1 -0.1958 7.55e-06 0.1544
## lst_wet df logLik AICc delta weight
## m6 0.001426 4 47.997 -86.4 0.00 0.750
## m5 4 45.752 -81.9 4.49 0.079
## m2 4 45.254 -80.9 5.49 0.048
## m4 4 45.231 -80.9 5.53 0.047
## m3 4 45.013 -80.4 5.97 0.038
## m1 4 45.010 -80.4 5.98 0.038
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m6 -0.2211 0.1551
## m5 -0.3170 0.1753 0.009211
## m2 -0.1753 0.1527 -0.05865
## m4 -0.2610 0.1700 -0.0007976
## m3 -0.1828 0.1521 -0.003951
## m1 -0.1958 7.55e-06 0.1544
## lst_wet df logLik AICc delta weight
## m6 0.001426 4 47.997 -86.4 0.00 0.750
## m5 4 45.752 -81.9 4.49 0.079
## m2 4 45.254 -80.9 5.49 0.048
## m4 4 45.231 -80.9 5.53 0.047
## m3 4 45.013 -80.4 5.97 0.038
## m1 4 45.010 -80.4 5.98 0.038
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m6 0.4497 0.01267
## m4 0.4199 0.02108 -0.0005937
## m1 0.3847 0.0005377 0.01886
## m3 0.4175 0.01593 0.1148
## m5 0.4460 0.01159 -0.002194
## m2 0.4377 0.01241 0.02937
## lst_wet df logLik AICc delta weight
## m6 -0.0006736 4 44.293 -79.0 0.00 0.227
## m4 4 44.045 -78.5 0.50 0.177
## m1 4 44.031 -78.5 0.52 0.175
## m3 4 43.937 -78.3 0.71 0.159
## m5 4 43.746 -77.9 1.09 0.131
## m2 4 43.737 -77.9 1.11 0.130
## Models ranked by AICc(x)
## Error in nrow(concord.out): object 'concord.out' not found
sites<-c("macae", "frenchguiana", "costarica", "colombia", "argentina")#many NA models
nocaprdata$log.co2.final<-log(nocaprdata$co2.final)
concord.out10<-concord.magic(sites, "log.co2.final", nocaprdata, 10, 2, "gaussian")#rain models hardly ever converge
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 3.778 -0.028350
## m1 4.879 -0.184700 0.09243
## m2 2.793 0.114400 -0.09847
## m3 3.798 -0.041350 0.19710 0.10030
## m4 4.504 -0.117400 -0.08564 -0.3488
## m9 3.559 0.006522 0.08888 -0.11430
## m10 5.463 -0.247700 0.21900 -0.09904 -0.4636
## m11 2.158 0.195700 0.19650 -0.15490 0.10380
## m12 7.292 -0.504200 0.16930 -0.09804 -0.05072 -0.9111
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m1
## m2
## m3
## m4
## m9 -0.08922
## m10 -0.08422 -0.39140
## m11 -0.05866
## m12 -0.06221 0.09083
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik AICc
## m0 3 -25.162 57.2
## m1 4 -24.714 59.0
## m2 4 -25.025 59.6
## m3 5 -24.146 60.8
## m4 5 -24.750 62.0
## m9 6 -24.458 64.6
## m10 8 -23.687 70.2
## m11 0.03201 8 -23.825 70.5
## m12 0.01584 0.4501 11 -22.511 81.7
## delta weight
## m0 0.00 0.499
## m1 1.78 0.205
## m2 2.40 0.150
## m3 3.55 0.085
## m4 4.75 0.046
## m9 7.32 0.013
## m10 12.98 0.001
## m11 13.26 0.001
## m12 24.44 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 3.778 -0.028350
## m1 4.879 -0.184700 0.09243
## m2 2.793 0.114400 -0.09847
## m3 3.798 -0.041350 0.19710 0.10030
## m4 4.504 -0.117400 -0.08564 -0.3488
## m9 3.559 0.006522 0.08888 -0.11430
## m10 5.463 -0.247700 0.21900 -0.09904 -0.4636
## m11 2.158 0.195700 0.19650 -0.15490 0.10380
## m12 7.292 -0.504200 0.16930 -0.09804 -0.05072 -0.9111
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m1
## m2
## m3
## m4
## m9 -0.08922
## m10 -0.08422 -0.39140
## m11 -0.05866
## m12 -0.06221 0.09083
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik AICc
## m0 3 -25.162 57.2
## m1 4 -24.714 59.0
## m2 4 -25.025 59.6
## m3 5 -24.146 60.8
## m4 5 -24.750 62.0
## m9 6 -24.458 64.6
## m10 8 -23.687 70.2
## m11 0.03201 8 -23.825 70.5
## m12 0.01584 0.4501 11 -22.511 81.7
## delta weight
## m0 0.00 0.499
## m1 1.78 0.205
## m2 2.40 0.150
## m3 3.55 0.085
## m4 4.75 0.046
## m9 7.32 0.013
## m10 12.98 0.001
## m11 13.26 0.001
## m12 24.44 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 3.778 -0.028350
## m1 4.879 -0.184700 0.09243
## m2 2.793 0.114400 -0.09847
## m3 3.798 -0.041350 0.19710 0.10030
## m4 4.504 -0.117400 -0.08564 -0.3488
## m9 3.559 0.006522 0.08888 -0.11430
## m10 5.463 -0.247700 0.21900 -0.09904 -0.4636
## m11 2.158 0.195700 0.19650 -0.15490 0.10380
## m12 7.292 -0.504200 0.16930 -0.09804 -0.05072 -0.9111
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m1
## m2
## m3
## m4
## m9 -0.08922
## m10 -0.08422 -0.39140
## m11 -0.05866
## m12 -0.06221 0.09083
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik AICc
## m0 3 -25.162 57.2
## m1 4 -24.714 59.0
## m2 4 -25.025 59.6
## m3 5 -24.146 60.8
## m4 5 -24.750 62.0
## m9 6 -24.458 64.6
## m10 8 -23.687 70.2
## m11 0.03201 8 -23.825 70.5
## m12 0.01584 0.4501 11 -22.511 81.7
## delta weight
## m0 0.00 0.499
## m1 1.78 0.205
## m2 2.40 0.150
## m3 3.55 0.085
## m4 4.75 0.046
## m9 7.32 0.013
## m10 12.98 0.001
## m11 13.26 0.001
## m12 24.44 0.000
## Models ranked by AICc(x)
## Warning in matrix(value, n, p): data length [11] is not a sub-multiple or
## multiple of the number of columns [10]
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 3.778 -0.028350
## m1 4.879 -0.184700 0.09243
## m2 2.793 0.114400 -0.09847
## m3 3.798 -0.041350 0.19710 0.10030
## m4 4.504 -0.117400 -0.08564 -0.3488
## m9 3.559 0.006522 0.08888 -0.11430
## m10 5.463 -0.247700 0.21900 -0.09904 -0.4636
## m11 2.158 0.195700 0.19650 -0.15490 0.10380
## m12 7.292 -0.504200 0.16930 -0.09804 -0.05072 -0.9111
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m1
## m2
## m3
## m4
## m9 -0.08922
## m10 -0.08422 -0.39140
## m11 -0.05866
## m12 -0.06221 0.09083
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik AICc
## m0 3 -25.162 57.2
## m1 4 -24.714 59.0
## m2 4 -25.025 59.6
## m3 5 -24.146 60.8
## m4 5 -24.750 62.0
## m9 6 -24.458 64.6
## m10 8 -23.687 70.2
## m11 0.03201 8 -23.825 70.5
## m12 0.01584 0.4501 11 -22.511 81.7
## delta weight
## m0 0.00 0.499
## m1 1.78 0.205
## m2 2.40 0.150
## m3 3.55 0.085
## m4 4.75 0.046
## m9 7.32 0.013
## m10 12.98 0.001
## m11 13.26 0.001
## m12 24.44 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 4.059 -0.1800
## m1 3.861 -0.1465 -0.06538
## m2 3.938 -0.1577 0.028440
## m4 4.488 -0.2354 0.014930 -0.4047
## m3 3.865 -0.1469 -0.06777 -0.002246
## m9 3.728 -0.1220 -0.06618 0.042850
## m10 5.083 -0.3392 0.10460 0.006053 -0.5536
## m11 3.652 -0.1076 -0.06863 -0.048130 -0.001842
## m12 5.276 -0.3929 0.23280 -0.090370 0.113600 -0.2327
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m1
## m2
## m4
## m3
## m9 0.03349
## m10 0.03740 -0.5111
## m11 0.14300
## m12 0.14100 -0.9217
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik AICc
## m0 3 -11.716 30.4
## m1 4 -11.156 31.9
## m2 4 -11.690 33.0
## m4 5 -10.715 33.9
## m3 5 -11.156 34.8
## m9 6 -11.066 37.8
## m10 8 -8.168 39.2
## m11 0.10250 8 -10.586 44.0
## m12 0.09583 -0.3697 11 -6.387 49.4
## delta weight
## m0 0.00 0.490
## m1 1.56 0.225
## m2 2.62 0.132
## m4 3.57 0.082
## m3 4.46 0.053
## m9 7.43 0.012
## m10 8.84 0.006
## m11 13.67 0.001
## m12 19.09 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 4.059 -0.1800
## m1 3.861 -0.1465 -0.06538
## m2 3.938 -0.1577 0.028440
## m4 4.488 -0.2354 0.014930 -0.4047
## m3 3.865 -0.1469 -0.06777 -0.002246
## m9 3.728 -0.1220 -0.06618 0.042850
## m10 5.083 -0.3392 0.10460 0.006053 -0.5536
## m11 3.652 -0.1076 -0.06863 -0.048130 -0.001842
## m12 5.276 -0.3929 0.23280 -0.090370 0.113600 -0.2327
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m1
## m2
## m4
## m3
## m9 0.03349
## m10 0.03740 -0.5111
## m11 0.14300
## m12 0.14100 -0.9217
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik AICc
## m0 3 -11.716 30.4
## m1 4 -11.156 31.9
## m2 4 -11.690 33.0
## m4 5 -10.715 33.9
## m3 5 -11.156 34.8
## m9 6 -11.066 37.8
## m10 8 -8.168 39.2
## m11 0.10250 8 -10.586 44.0
## m12 0.09583 -0.3697 11 -6.387 49.4
## delta weight
## m0 0.00 0.490
## m1 1.56 0.225
## m2 2.62 0.132
## m4 3.57 0.082
## m3 4.46 0.053
## m9 7.43 0.012
## m10 8.84 0.006
## m11 13.67 0.001
## m12 19.09 0.000
## Models ranked by AICc(x)
## Warning in matrix(value, n, p): data length [11] is not a sub-multiple or
## multiple of the number of columns [10]
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 4.059 -0.1800
## m1 3.861 -0.1465 -0.06538
## m2 3.938 -0.1577 0.028440
## m4 4.488 -0.2354 0.014930 -0.4047
## m3 3.865 -0.1469 -0.06777 -0.002246
## m9 3.728 -0.1220 -0.06618 0.042850
## m10 5.083 -0.3392 0.10460 0.006053 -0.5536
## m11 3.652 -0.1076 -0.06863 -0.048130 -0.001842
## m12 5.276 -0.3929 0.23280 -0.090370 0.113600 -0.2327
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m1
## m2
## m4
## m3
## m9 0.03349
## m10 0.03740 -0.5111
## m11 0.14300
## m12 0.14100 -0.9217
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik AICc
## m0 3 -11.716 30.4
## m1 4 -11.156 31.9
## m2 4 -11.690 33.0
## m4 5 -10.715 33.9
## m3 5 -11.156 34.8
## m9 6 -11.066 37.8
## m10 8 -8.168 39.2
## m11 0.10250 8 -10.586 44.0
## m12 0.09583 -0.3697 11 -6.387 49.4
## delta weight
## m0 0.00 0.490
## m1 1.56 0.225
## m2 2.62 0.132
## m4 3.57 0.082
## m3 4.46 0.053
## m9 7.43 0.012
## m10 8.84 0.006
## m11 13.67 0.001
## m12 19.09 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 -0.6484 0.5580
## m1 -0.4485 0.5155 -0.076710
## m2 -0.6496 0.5582 0.004230
## m3 -0.6371 0.5369 0.016040 0.08625
## m9 -2.5550 0.9243 -0.069080 0.083900
## m4 -0.6288 0.5748 0.004337 -0.3325
## m11 -2.5030 0.8986 0.025910 0.030150 0.08915
## m10 -2.7530 0.9864 -0.028020 0.086350 -0.3781
## m12 -2.9310 1.0180 -0.008439 0.043120 0.01605 -0.5913
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m1
## m2
## m3
## m9 0.3157
## m4
## m11 0.3742
## m10 0.3241 -0.1246
## m12 0.3806 0.1174
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik AICc
## m0 3 -22.882 52.7
## m1 4 -22.517 54.6
## m2 4 -22.882 55.4
## m3 5 -22.022 56.5
## m9 6 -20.740 57.1
## m4 5 -22.563 57.6
## m11 0.05787 8 -20.061 63.0
## m10 8 -20.286 63.4
## m12 0.04882 0.2312 11 -19.377 75.4
## delta weight
## m0 0.00 0.503
## m1 1.95 0.190
## m2 2.68 0.132
## m3 3.86 0.073
## m9 4.45 0.054
## m4 4.94 0.043
## m11 10.29 0.003
## m10 10.74 0.002
## m12 22.73 0.000
## Models ranked by AICc(x)
## Warning in matrix(value, n, p): data length [11] is not a sub-multiple or
## multiple of the number of columns [10]
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 -0.6484 0.5580
## m1 -0.4485 0.5155 -0.076710
## m2 -0.6496 0.5582 0.004230
## m3 -0.6371 0.5369 0.016040 0.08625
## m9 -2.5550 0.9243 -0.069080 0.083900
## m4 -0.6288 0.5748 0.004337 -0.3325
## m11 -2.5030 0.8986 0.025910 0.030150 0.08915
## m10 -2.7530 0.9864 -0.028020 0.086350 -0.3781
## m12 -2.9310 1.0180 -0.008439 0.043120 0.01605 -0.5913
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m1
## m2
## m3
## m9 0.3157
## m4
## m11 0.3742
## m10 0.3241 -0.1246
## m12 0.3806 0.1174
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik AICc
## m0 3 -22.882 52.7
## m1 4 -22.517 54.6
## m2 4 -22.882 55.4
## m3 5 -22.022 56.5
## m9 6 -20.740 57.1
## m4 5 -22.563 57.6
## m11 0.05787 8 -20.061 63.0
## m10 8 -20.286 63.4
## m12 0.04882 0.2312 11 -19.377 75.4
## delta weight
## m0 0.00 0.503
## m1 1.95 0.190
## m2 2.68 0.132
## m3 3.86 0.073
## m9 4.45 0.054
## m4 4.94 0.043
## m11 10.29 0.003
## m10 10.74 0.002
## m12 22.73 0.000
## Models ranked by AICc(x)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 0.5714 0.0128700
## m2 0.6435 0.0007826 -0.02428
## m1 0.5672 0.0142100 0.01044
## m4 0.6431 -0.0002612 -0.02479 0.01482
## m3 0.5702 0.0139500 0.00902 -0.0010930
## m9 0.6468 0.0011760 0.01101 -0.03033
## m10 0.6478 0.0045040 0.01299 -0.02898 -0.04557
## m11 0.6484 0.0007111 0.01175 -0.03470 0.0006637
## m12 0.6517 0.0039690 0.01218 -0.03264 -0.0005669 -0.04641
## log(k.scl):log(mu.scl) log(mu.scl)^2):log(k.scl df logLik AICc delta
## m0 3 21.822 -35.2 0.00
## m2 4 22.357 -32.3 2.97
## m1 4 22.299 -32.2 3.09
## m4 5 22.366 -27.2 8.01
## m3 5 22.303 -27.1 8.14
## m9 -0.01988 6 23.767 -23.5 11.71
## m10 -0.01959 7 23.846 -15.0 20.22
## m11 -0.01597 0.003346 8 23.790 -2.8 32.46
## m12 -0.01603 0.002993 9 23.865 15.3 50.51
## weight
## m0 0.677
## m2 0.153
## m1 0.144
## m4 0.012
## m3 0.012
## m9 0.002
## m10 0.000
## m11 0.000
## m12 0.000
## Models ranked by AICc(x)
## Warning in matrix(value, n, p): data length [11] is not a sub-multiple or
## multiple of the number of columns [10]
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m1 3.177 -0.002996 0.075260
## m4 3.583 -0.040640 0.004358
## m3 3.445 0.023910 -0.6007
## m5 3.474 0.019360 -0.01388
## m2 3.647 0.001619 -1.208
## m6 3.662 -0.004729
## lst_wet df logLik AICc delta weight
## m1 4 -24.677 59.0 0.00 0.203
## m4 4 -24.789 59.2 0.22 0.181
## m3 4 -24.805 59.2 0.26 0.179
## m5 4 -24.878 59.4 0.40 0.166
## m2 4 -25.049 59.7 0.74 0.140
## m6 -0.001561 4 -25.114 59.8 0.87 0.131
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m1 3.177 -0.002996 0.075260
## m4 3.583 -0.040640 0.004358
## m3 3.445 0.023910 -0.6007
## m5 3.474 0.019360 -0.01388
## m2 3.647 0.001619 -1.208
## m6 3.662 -0.004729
## lst_wet df logLik AICc delta weight
## m1 4 -24.677 59.0 0.00 0.203
## m4 4 -24.789 59.2 0.22 0.181
## m3 4 -24.805 59.2 0.26 0.179
## m5 4 -24.878 59.4 0.40 0.166
## m2 4 -25.049 59.7 0.74 0.140
## m6 -0.001561 4 -25.114 59.8 0.87 0.131
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m1 3.177 -0.002996 0.075260
## m4 3.583 -0.040640 0.004358
## m3 3.445 0.023910 -0.6007
## m5 3.474 0.019360 -0.01388
## m2 3.647 0.001619 -1.208
## m6 3.662 -0.004729
## lst_wet df logLik AICc delta weight
## m1 4 -24.677 59.0 0.00 0.203
## m4 4 -24.789 59.2 0.22 0.181
## m3 4 -24.805 59.2 0.26 0.179
## m5 4 -24.878 59.4 0.40 0.166
## m2 4 -25.049 59.7 0.74 0.140
## m6 -0.001561 4 -25.114 59.8 0.87 0.131
## Models ranked by AICc(x)
## Warning in matrix(value, n, p): data length [11] is not a sub-multiple or
## multiple of the number of columns [10]
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m1 3.177 -0.002996 0.075260
## m4 3.583 -0.040640 0.004358
## m3 3.445 0.023910 -0.6007
## m5 3.474 0.019360 -0.01388
## m2 3.647 0.001619 -1.208
## m6 3.662 -0.004729
## lst_wet df logLik AICc delta weight
## m1 4 -24.677 59.0 0.00 0.203
## m4 4 -24.789 59.2 0.22 0.181
## m3 4 -24.805 59.2 0.26 0.179
## m5 4 -24.878 59.4 0.40 0.166
## m2 4 -25.049 59.7 0.74 0.140
## m6 -0.001561 4 -25.114 59.8 0.87 0.131
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m3 4.553 -0.2653 -1.571
## m6 4.436 -0.2418
## m4 4.699 -0.3465 0.005307
## m1 4.720 -0.003668 -0.2848
## m2 3.951 -0.1500 -0.2236
## m5 4.176 -0.2001 -0.008727
## lst_wet df logLik AICc delta weight
## m3 4 -11.098 31.8 0.00 0.217
## m6 -0.008915 4 -11.178 32.0 0.16 0.200
## m4 4 -11.324 32.2 0.45 0.173
## m1 4 -11.449 32.5 0.70 0.153
## m2 4 -11.569 32.7 0.94 0.135
## m5 4 -11.671 32.9 1.15 0.122
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m3 4.553 -0.2653 -1.571
## m6 4.436 -0.2418
## m4 4.699 -0.3465 0.005307
## m1 4.720 -0.003668 -0.2848
## m2 3.951 -0.1500 -0.2236
## m5 4.176 -0.2001 -0.008727
## lst_wet df logLik AICc delta weight
## m3 4 -11.098 31.8 0.00 0.217
## m6 -0.008915 4 -11.178 32.0 0.16 0.200
## m4 4 -11.324 32.2 0.45 0.173
## m1 4 -11.449 32.5 0.70 0.153
## m2 4 -11.569 32.7 0.94 0.135
## m5 4 -11.671 32.9 1.15 0.122
## Models ranked by AICc(x)
## Warning in matrix(value, n, p): data length [11] is not a sub-multiple or
## multiple of the number of columns [10]
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m3 4.553 -0.2653 -1.571
## m6 4.436 -0.2418
## m4 4.699 -0.3465 0.005307
## m1 4.720 -0.003668 -0.2848
## m2 3.951 -0.1500 -0.2236
## m5 4.176 -0.2001 -0.008727
## lst_wet df logLik AICc delta weight
## m3 4 -11.098 31.8 0.00 0.217
## m6 -0.008915 4 -11.178 32.0 0.16 0.200
## m4 4 -11.324 32.2 0.45 0.173
## m1 4 -11.449 32.5 0.70 0.153
## m2 4 -11.569 32.7 0.94 0.135
## m5 4 -11.671 32.9 1.15 0.122
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m5 -2.3770 0.8553 0.1287
## m1 -1.3230 0.002036 0.6648
## m2 -0.7154 0.5627 0.218
## m6 -0.6550 0.5582
## m4 -0.5945 0.5452 0.0006345
## m3 -0.6765 0.5630 0.01055
## lst_wet df logLik AICc delta weight
## m5 4 -21.269 52.1 0.00 0.492
## m1 4 -22.741 55.1 2.94 0.113
## m2 4 -22.846 55.3 3.15 0.102
## m6 0.0003391 4 -22.881 55.4 3.22 0.098
## m4 4 -22.881 55.4 3.22 0.098
## m3 4 -22.882 55.4 3.23 0.098
## Models ranked by AICc(x)
## Warning in matrix(value, n, p): data length [11] is not a sub-multiple or
## multiple of the number of columns [10]
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lng_dry
## m5 -2.3770 0.8553 0.1287
## m1 -1.3230 0.002036 0.6648
## m2 -0.7154 0.5627 0.218
## m6 -0.6550 0.5582
## m4 -0.5945 0.5452 0.0006345
## m3 -0.6765 0.5630 0.01055
## lst_wet df logLik AICc delta weight
## m5 4 -21.269 52.1 0.00 0.492
## m1 4 -22.741 55.1 2.94 0.113
## m2 4 -22.846 55.3 3.15 0.102
## m6 0.0003391 4 -22.881 55.4 3.22 0.098
## m4 4 -22.881 55.4 3.22 0.098
## m3 4 -22.882 55.4 3.23 0.098
## Models ranked by AICc(x)
## Model selection table
## (Int) cv.dpt log(mxv) prp.ovr.dys prp.drd.dys men.dpt lst_wet df
## m6 0.5686 0.009207 0.00189 4
## m5 0.5714 0.012870 3
## m3 0.5977 0.010850 -5.951 4
## m1 0.4768 0.001575 0.021260 4
## m4 0.5735 0.016740 -0.0004211 4
## m2 0.5714 0.012860 0.0002784 4
## logLik AICc delta weight
## m6 24.454 -36.5 0.00 0.469
## m5 21.822 -35.2 1.22 0.255
## m3 23.281 -34.1 2.35 0.145
## m1 22.432 -32.4 4.04 0.062
## m4 21.887 -31.3 5.13 0.036
## m2 21.822 -31.2 5.26 0.034
## Models ranked by AICc(x)
## Warning in matrix(value, n, p): data length [11] is not a sub-multiple or
## multiple of the number of columns [10]
## Error in nrow(concord.out): object 'concord.out' not found
concord.out.all<-rbind(concord.out1, concord.out2, concord.out3, concord.out4, concord.out5, concord.out6, concord.out7, concord.out9, concord.out10)
## Error in rbind(concord.out1, concord.out2, concord.out3, concord.out4, : object 'concord.out2' not found
concord.out.all%>%filter(Sites==1)%>%select(Response, Reference, Target, Model, Kendall)%>%filter(Kendall>0.65)
## Error in eval(expr, envir, enclos): object 'concord.out.all' not found
#both scrapers and gatherers in colombbia are well predicted by other sites....?why?
tapply(fulldata$maxvol, fulldata$site, min)
## argentina cardoso colombia costarica frenchguiana
## 185 700 73 110 110
## macae puertorico
## 850 230
tapply(fulldata$maxvol, fulldata$site, max)
## argentina cardoso colombia costarica frenchguiana
## 600 1315 1726 225 338
## macae puertorico
## 1400 550
codata$maxvol
## [1] 1194.0000 274.0000 330.0000 582.0000 1726.0000 483.0000 380.0000
## [8] 988.0000 358.0000 354.0000 202.0000 189.0000 263.0000 211.0000
## [15] 237.0000 265.0000 278.0000 217.0000 790.0000 73.0000 411.0000
## [22] 341.0000 438.0000 180.0000 200.0000 137.0000 453.0000 299.0000
## [29] 564.3761 190.0000
codata$gatherer_bio
## [1] 13.6118510 0.3425567 1.1727664 13.5113657 12.1870160 6.6276201
## [7] 58.7786936 5.4329512 2.2861763 0.9343690 4.3279909 3.4228160
## [13] 2.3958522 6.2171105 10.9262650 3.1824794 0.1311561 1.2596926
## [19] 12.1587928 0.2227189 2.8524737 10.2957645 3.3122189 1.6275187
## [25] 1.0175999 4.2224427 28.2749070 2.6811994 16.5011645 0.8901472
plot(log(codata$gatherer_bio)~log(codata$maxvol))
plot(log(codata$scraper_bio)~log(codata$maxvol))
cor.test(prdata$gatherer_bio, prdata$maxvol, method="kendall")
## Warning in cor.test.default(prdata$gatherer_bio, prdata$maxvol, method =
## "kendall"): Cannot compute exact p-value with ties
##
## Kendall's rank correlation tau
##
## data: prdata$gatherer_bio and prdata$maxvol
## z = 0.59189, p-value = 0.5539
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.07765759
cor.test(fgdata$scraper_bio, fgdata$maxvol, method="kendall")
## Warning in cor.test.default(fgdata$scraper_bio, fgdata$maxvol, method =
## "kendall"): Cannot compute exact p-value with ties
##
## Kendall's rank correlation tau
##
## data: fgdata$scraper_bio and fgdata$maxvol
## z = 1.3209, p-value = 0.1865
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1707046
#consider cohen.kappa(cbind(x,y))
mean.concord<-concord.out.all %>%
group_by(Response,Model, Sites) %>%
summarise(mCCC = mean(CCC, na.rm=TRUE), mSpearman=mean(Spearman, na.rm=TRUE), mKendall=mean(Kendall, na.rm=TRUE), mPearson=mean(Precision, na.rm=TRUE))
## Error in eval(expr, envir, enclos): object 'concord.out.all' not found
ggplot(mean.concord, aes(Sites, mSpearman, linetype=Model, colour=Response)) +
geom_point(size = 3) +
geom_smooth(span=1.2,se=FALSE)
## Error in ggplot(mean.concord, aes(Sites, mSpearman, linetype = Model, : object 'mean.concord' not found
ggplot(mean.concord, aes(Sites, mKendall, linetype=Model, colour=Response)) +
geom_point(size = 3) +
geom_smooth(span=1.2,se=FALSE)
## Error in ggplot(mean.concord, aes(Sites, mKendall, linetype = Model, colour = Response)): object 'mean.concord' not found
ggplot(mean.concord, aes(Sites, mPearson, linetype=Model, colour=Response)) +
geom_point(size = 3) +
geom_smooth(span=1.2,se=FALSE)
## Error in ggplot(mean.concord, aes(Sites, mPearson, linetype = Model, colour = Response)): object 'mean.concord' not found
ggplot(concord.out.all, aes(Sites, Kendall, linetype=Model, colour=Response)) +
geom_jitter(width=0.25, size = 1, alpha = 1/3) +
geom_smooth(span=1.2, se=FALSE)
## Error in ggplot(concord.out.all, aes(Sites, Kendall, linetype = Model, : object 'concord.out.all' not found
####
tapply(nocadata$engulfer_bio, nocadata$site, datacheck)
## argentina cardoso colombia costarica frenchguiana
## 6 NA 10 19 26
## macae puertorico
## 30 12
#===#taxonomy explain conting?-------------------------
bestga<-glm.nb(round(gatherer_bio*10)~log(maxvol)+site*(log(k.scalar)+I(log(k.scalar)^2)), data = no67185data)
betterga<-glm.nb(round(gatherer_bio*10)~log(maxvol)+site*I(log(k.scalar)^2), data = no67185data)
taxonga<-glm.nb(round(gatherer_bio*10)~log(maxvol)+site+detCerato_bio*I(log(k.scalar)^2)+Ephydridae_bio*I(log(k.scalar)^2)+
Psychodidae_bio*I(log(k.scalar)^2)+Stratiomyidae_bio*I(log(k.scalar)^2)+Syrphidae_bio*I(log(k.scalar)^2)+
Enchytraeoidae_bio*I(log(k.scalar)^2)+Naididae_bio*I(log(k.scalar)^2)+detChiron_bio*I(log(k.scalar)^2)+
site*I(log(k.scalar)^2), data = no67185data)#driven by worms and psychodid
## Warning: glm.fit: algorithm did not converge
## Warning in glm.nb(round(gatherer_bio * 10) ~ log(maxvol) + site +
## detCerato_bio * : alternation limit reached
taxonga<-glm.nb(round(gatherer_bio*10)~log(maxvol)+site+I(log(k.scalar)^2)+Psychodidae_bio*I(log(k.scalar)^2)+
site:I(log(k.scalar)^2), data = no67185data)
p1<-glm(round(argcacodata$Psychodidae_bio*100)~log(maxvol)+site*I(log(k.scalar)^2), family=negative.binomial(theta = 0.2222), argcacodata)
visreg(taxonga)
## Please note that you are attempting to plot a 'main effect' in a
## model that contains an interaction. This is potentially
## misleading; you may wish to consider using the 'by' argument.
##
## Conditions used in construction of plot
## log.maxvol.: 4.29045944114839 / 4.49457376464606 / 4.66400497170898 / 4.8088454541069 / 4.93533687004603 / 5.04760989688762 / 5.14854044122981 / 5.240211445141 / 5.32418002607376 / 5.40164080877994 / 5.4735303841047 / 5.54059665617072 / 5.60344635260354 / 5.66257846273214 / 5.71840832685152 / 5.77128534775779 / 5.82150624929743 / 5.86932516132236 / 5.91496140112187 / 5.9586055552262 / 6.00042428847945 / 6.04056418719433 / 6.07915486023497 / 6.11631146359708 / 6.15213677249688 / 6.18672289493266 / 6.22015269867754 / 6.25250100735657 / 6.28383560904589 / 6.31421811158826 / 6.3437046717566 / 6.37234661995529 / 6.40019099791852 / 6.42728102355309 / 6.45365649445969 / 6.47935413959091 / 6.50440792684497 / 6.52884933305922 / 6.55270758178756 / 6.57600985336731 / 6.59878147106273 / 6.62104606648195 / 6.64282572697633 / 6.66414112732742 / 6.68501164768925 / 6.70545547947252 / 6.72548972062024 / 6.74513046152549 / 6.76439286267278 / 6.78329122494186 / 6.80183905339059 / 6.82004911522953 / 6.83793349261158 / 6.85550363078339 / 6.87277038207918 / 6.88974404618029 / 6.90643440701472 / 6.92285076662747 / 6.93900197631531 / 6.95489646528694 / 6.9705422670809 / 6.98594704394839 / 7.00111810938635 / 7.01606244898671 / 7.03078673975053 / 7.04529736800062 / 7.05960044601318 / 7.07370182747656 / 7.08760712187516 / 7.10132170788699 / 7.11485074587474 / 7.12819918954323 / 7.14137179682892 / 7.15437314008153 / 7.16720761559221 / 7.17987945251797 / 7.19239272124769 / 7.20475134125121 / 7.21695908844916 / 7.22901960213847 / 7.24093639150514 / 7.25271284175348 / 7.26435221987871 / 7.27585768010736 / 7.28723226902831 / 7.29847893043527 / 7.3096005098999 / 7.32059975909354 / 7.33147933987369 / 7.3422418281507 / 7.35288971754851 / 7.36342542287254 / 7.37385128339686 / 7.38416956598169 / 7.39438246803175 / 7.40449212030503 / 7.41450058958098 / 7.42440988119657 / 7.43422194145787 / 7.44393865993444 / 7.45356187164337
## site: argentina
## I.log.k.scalar..2.: 0
## Psychodidae_bio: 0
## gatherer_bio: 3.027027
## Please note that you are attempting to plot a 'main effect' in a
## model that contains an interaction. This is potentially
## misleading; you may wish to consider using the 'by' argument.
##
## Conditions used in construction of plot
## log.maxvol.: 5.821566
## I.log.k.scalar..2.: 0
## Psychodidae_bio: 0
## gatherer_bio: 3.027027
## maxvol: 337.5
## Please note that you are attempting to plot a 'main effect' in a
## model that contains an interaction. This is potentially
## misleading; you may wish to consider using the 'by' argument.
##
## Conditions used in construction of plot
## I.log.k.scalar..2.: 0.480453013918201 / 0.440349535839245 / 0.403070420811281 / 0.368411954894343 / 0.336189487437386 / 0.306235221840833 / 0.278396310517363 / 0.252533205775416 / 0.228518226983215 / 0.206234311294118 / 0.185573920797118 / 0.166438083483051 / 0.148735549106055 / 0.132382044040922 / 0.117299611722458 / 0.103416027306953 / 0.090664276900414 / 0.07898209311849 / 0.0683115399310345 / 0.058598640741674 / 0.0497930444931173 / 0.041847725299418 / 0.0347187117090199 / 0.0283648422151707 / 0.0227475440678904 / 0.0178306328162444 / 0.0135801303311759 / 0.009964099335853 / 0.00695249270926485 / 0.00451701603535681 / 0.00263100204912793 / 0.00126929578681665 / 0.000408149382957355 / 2.51255755327807e-05 / 9.90090840875088e-05 / 0.000609725116538169 / 0.00153826434025198 / 0.00286661372324161 / 0.00457769271330322 / 0.00665529427768388 / 0.00908403037433275 / 0.0118492814687635 / 0.0149371497487194 / 0.0183344157227816 / 0.0220284979193082 / 0.0260074154290846 / 0.0302597530591811 / 0.0347746288871065 / 0.0395416640236896 / 0.0445509544104871 / 0.0497930444931174 / 0.0552589026259661 / 0.060939898076359 / 0.0668277795077123 / 0.0729146548314809 / 0.0791929723270484 / 0.085655502937146 / 0.0922953236540343 / 0.0991058019186293 / 0.106080580961061 / 0.113213566016881 / 0.120498911358374 / 0.127931008085152 / 0.135504472622599 / 0.143214135880643 / 0.15105503302899 / 0.159022393848253 / 0.167111633619442 / 0.175318344517053 / 0.183638287473568 / 0.192067384485472 / 0.200601711333097 / 0.209237490688539 / 0.217971085587716 / 0.226798993244348 / 0.235717839185118 / 0.244724371686768 / 0.253815456497145 / 0.262988071823446 / 0.272239303572045 / 0.281566340825298 / 0.290966471541696 / 0.300437078466644 / 0.309975635241918 / 0.319579702702688 / 0.329246925351639 / 0.338975028000421 / 0.348761812569265 / 0.358605155036161 / 0.368503002527536 / 0.378453370542859 / 0.388454340306059 / 0.398504056237076 / 0.408600723537254 / 0.418742605882671 / 0.428928023219845 / 0.439155349658566 / 0.449423011456939 / 0.459729485093977 / 0.470073295425352
## log.maxvol.: 5.821566
## site: argentina
## Psychodidae_bio: 0
## gatherer_bio: 3.027027
## Please note that you are attempting to plot a 'main effect' in a
## model that contains an interaction. This is potentially
## misleading; you may wish to consider using the 'by' argument.
##
## Conditions used in construction of plot
## log.maxvol.: 5.821566
## site: argentina
## I.log.k.scalar..2.: 0
## gatherer_bio: 3.027027
## maxvol: 337.5
no67185data$sensgath<-no67185data$detCerato_bio+no67185data$Ephydridae_bio+no67185data$Stratiomyidae_bio+no67185data$Syrphidae_bio+no67185data$detChiron_bio
no67185data$tolgath<-no67185data$gatherer_bio+no67185data$sensgath
taxonga<-glm.nb(round(sensgath*10)~log(maxvol)+site*I(log(k.scalar)^2), data = no67185data)
Anova(taxonga)#al sig
## Analysis of Deviance Table (Type II tests)
##
## Response: round(sensgath * 10)
## LR Chisq Df Pr(>Chisq)
## log(maxvol) 15.85 1 6.843e-05 ***
## site 345.82 6 < 2.2e-16 ***
## I(log(k.scalar)^2) 4.11 1 0.0426361 *
## site:I(log(k.scalar)^2) 23.25 6 0.0007162 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
taxonga<-glm.nb(round(tolgath*10)~log(maxvol)+site*I(log(k.scalar)^2), data = no67185data)
taxonga<-glm.nb(round(detCerato_bio*10)~log(maxvol)+site*I(log(k.scalar)^2), data = no67185data)#no
taxonga<-glm.nb(round(Enchytraeoidae_bio*10)~log(maxvol)+site*I(log(k.scalar)^2), data = no67185data)#no
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in sqrt(1/i): NaNs produced
## Warning in glm.nb(round(Enchytraeoidae_bio * 10) ~ log(maxvol) + site * :
## alternation limit reached
par(mfrow=c(2,2)); plot(taxonga)
#====taxonomic groups ------------------------
tapply(fulldata$Odonata_bio, fulldata$site, datacheck)#only 2 (CA,MA)-5 sites with Odonates
## argentina cardoso colombia costarica frenchguiana
## 0 26 4 8 2
## macae puertorico
## 18 0
tapply(fulldata$Corethrellidae_bio, fulldata$site, datacheck)
## argentina cardoso colombia costarica frenchguiana
## 0 30 0 0 24
## macae puertorico
## 30 0
tapply(fulldata$engulfer_bio, fulldata$site, datacheck)
## argentina cardoso colombia costarica frenchguiana
## 6 30 10 19 26
## macae puertorico
## 30 12
engulf.out<-data.frame(full.d2=numeric(2),noint.d2=numeric(2), intend.d2=numeric(2), site.d2=numeric(2), conting.p=numeric(2),general.p=numeric(2), siterain.p=numeric(2), rainfall.p=numeric(2))
engulf.out[1,]<-allmodel(2,camadata$Odonata_bio,poisson, camadata); #mild conting p=0.012
## Error in eval(expr, envir, enclos): could not find function "allmodel"
engulf.out[2,]<-allmodel(2,cafgmadata$Corethrellidae_bio,poisson, cafgmadata);row.names(engulf.out)<-c("Odonata biomass","Corethrellidae biomass")#no conting, strong effects of rain and site
## Error in eval(expr, envir, enclos): could not find function "allmodel"
kable(engulf.out[,c(1,5,6,7,8)])
|
full.d2 |
conting.p |
general.p |
siterain.p |
rainfall.p |
| Odonata biomass |
0 |
0 |
0 |
0 |
0 |
| Corethrellidae biomass |
0 |
0 |
0 |
0 |
0 |
m25a<-glm(engulfer_bio~maxvol+site*(mu.scalar+I(mu.scalar^2))*(k.scalar+I(k.scalar^2)), family=poisson, data=noargdata);
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.681149
## Warning in dpois(y, mu, log = TRUE): non-integer x = 7.985818
## Warning in dpois(y, mu, log = TRUE): non-integer x = 7.582096
## Warning in dpois(y, mu, log = TRUE): non-integer x = 8.143779
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.428571
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.625965
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.756271
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.823779
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.026067
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.121874
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.925850
## Warning in dpois(y, mu, log = TRUE): non-integer x = 6.797223
## Warning in dpois(y, mu, log = TRUE): non-integer x = 23.319184
## Warning in dpois(y, mu, log = TRUE): non-integer x = 11.111781
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.358095
## Warning in dpois(y, mu, log = TRUE): non-integer x = 7.563435
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.029604
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.692350
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.693541
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.956215
## Warning in dpois(y, mu, log = TRUE): non-integer x = 6.130740
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.759380
## Warning in dpois(y, mu, log = TRUE): non-integer x = 10.305714
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.738095
## Warning in dpois(y, mu, log = TRUE): non-integer x = 6.382826
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.934366
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.966636
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.518176
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.886292
## Warning in dpois(y, mu, log = TRUE): non-integer x = 6.507644
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.140600
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.570300
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.312387
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.667421
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.390100
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.618714
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.032000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.032000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.032000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.093084
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.063300
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.954000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.063000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.283500
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.126000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.157500
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.094500
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.094500
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.409500
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.063300
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.094500
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.157500
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.157500
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.063000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.031500
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.414500
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.063000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.189000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.031500
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.346500
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.067500
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.135300
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.063000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.031500
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.031800
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.063000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.782109
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.942280
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.369950
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.378291
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.416756
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.643117
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.136710
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.477489
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.671850
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.481771
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.347579
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.775923
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.910463
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.713403
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.583003
## Warning in dpois(y, mu, log = TRUE): non-integer x = 6.045818
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.433226
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.879961
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.644349
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.137859
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.633222
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.451262
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.477425
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.482642
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.567822
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.476477
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.976536
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.537797
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.075018
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.055545
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.520000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.480000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.220000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.520000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.255000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.780000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.780000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.260000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.673000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.260000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.480000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.260000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.432701
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.382701
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.292308
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.124392
## Warning in dpois(y, mu, log = TRUE): non-integer x = 8.311123
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.693749
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.389580
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.937591
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.098187
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.693749
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.050000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.050000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.693749
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.037817
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.846449
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.124392
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.050000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.382701
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.400438
padj<-m25a$deviance/m25a$df.residual
m25b<-glm((engulfer_bio/padj)~maxvol+site*(mu.scalar+I(mu.scalar^2))*(k.scalar+I(k.scalar^2)), family=poisson, data=noargdata);
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.629821
## Warning in dpois(y, mu, log = TRUE): non-integer x = 7.741998
## Warning in dpois(y, mu, log = TRUE): non-integer x = 7.350602
## Warning in dpois(y, mu, log = TRUE): non-integer x = 7.895136
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.415486
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.454195
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.580522
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.707032
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.903144
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.965495
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.744924
## Warning in dpois(y, mu, log = TRUE): non-integer x = 6.589693
## Warning in dpois(y, mu, log = TRUE): non-integer x = 22.607210
## Warning in dpois(y, mu, log = TRUE): non-integer x = 10.772520
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.316630
## Warning in dpois(y, mu, log = TRUE): non-integer x = 7.332511
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.876042
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.549085
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.580771
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.835425
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.943559
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.583537
## Warning in dpois(y, mu, log = TRUE): non-integer x = 9.991064
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.715560
## Warning in dpois(y, mu, log = TRUE): non-integer x = 6.187948
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.814243
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.814996
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.349697
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.737106
## Warning in dpois(y, mu, log = TRUE): non-integer x = 6.308955
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.044712
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.461293
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.272318
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.647043
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.378190
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.599824
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.031023
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.031023
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.031023
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.998647
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.061367
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.924873
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.061077
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.274844
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.122153
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.152691
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.091615
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.091615
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.396997
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.061367
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.091615
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.152691
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.152691
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.061077
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.030538
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.401845
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.061077
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.183230
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.030538
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.335921
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.065439
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.131169
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.061077
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.030538
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.030829
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.061077
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.758230
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.913511
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.328123
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.336210
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.373500
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.592950
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.102005
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.462910
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.651337
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.467062
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.336967
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.752233
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.882665
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.691622
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.565203
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.861229
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.419999
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.792031
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.624676
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.133650
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.613889
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.437484
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.462849
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.467906
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.550486
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.461930
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.946720
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.521377
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.072727
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.053849
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.504124
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.465345
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.213283
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.504124
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.247214
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.756185
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.756185
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.252062
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.591389
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.252062
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.465345
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.252062
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.419490
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.371017
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.252852
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.028999
## Warning in dpois(y, mu, log = TRUE): non-integer x = 8.057371
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.672568
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.347154
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.908964
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.095189
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.672568
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.048473
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.048473
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.672568
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.914535
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.790074
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.028999
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.048473
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.371017
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.357681
par(mfrow = c(1, 2))
visreg(m25b, "mu.scalar", by="site", overlay=TRUE, partial=FALSE, ylab="Engulfer biomass")
visreg(m25b, "k.scalar", by="site", overlay=TRUE, partial=FALSE, ylab="Engulfer biomass")
#costa rica is peagreen, french guiana is dk green, cardoso is red, macae is baby blue, puertotico is purple
m25a<-glm(Odonata_bio~maxvol+site*(mu.scalar+I(mu.scalar^2))*(k.scalar+I(k.scalar^2)), family=poisson, data=camadata);
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.429720
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.520104
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.271620
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.311398
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.625965
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.791509
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.311398
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.454638
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.311398
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.031564
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.791509
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.031564
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.454638
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.529150
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.791509
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.311398
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.311398
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.551453
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.702169
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.461285
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.311398
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.791509
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.311398
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.791509
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.529150
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.551453
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.230704
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.143966
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.083721
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.149481
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.042114
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.066071
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.020999
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.014925
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.328254
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.561069
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.784415
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.008183
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.008183
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.027271
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.020819
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.036623
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.017726
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.006453
padj<-m25a$deviance/m25a$df.residual
m25b<-glm((Odonata_bio/padj)~maxvol+site*(mu.scalar+I(mu.scalar^2))*(k.scalar+I(k.scalar^2)), family=poisson, data=camadata);
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.479587
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.044637
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.767318
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.046738
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.855530
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.499406
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.499406
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.855530
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.938688
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.963580
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.247830
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.978993
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.938688
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.963580
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.257476
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.160672
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.093436
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.282872
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.047001
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.073738
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.023436
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.016657
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.366346
## Warning in dpois(y, mu, log = TRUE): non-integer x = 6.206400
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.991486
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.009132
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.009132
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.030436
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.023235
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.040873
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.019783
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.007201
visreg(m25b, "mu.scalar", by="site", overlay=FALSE, partial=FALSE, ylab="odonata biomass")
visreg(m25b, "k.scalar", by="site", overlay=FALSE, partial=FALSE, ylab="odonata biomass")
anova(m25b, test="LRT")
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.479587
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.044637
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.767318
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.046738
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.855530
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.499406
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.499406
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.855530
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.938688
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.963580
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.247830
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.978993
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.938688
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.963580
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.257476
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.160672
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.093436
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.282872
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.047001
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.073738
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.023436
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.016657
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.366346
## Warning in dpois(y, mu, log = TRUE): non-integer x = 6.206400
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.991486
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.009132
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.009132
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.030436
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.023235
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.040873
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.019783
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.007201
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.479587
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.044637
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.767318
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.046738
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.855530
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.499406
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.499406
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.855530
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.938688
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.963580
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.247830
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.978993
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.938688
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.963580
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.257476
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.160672
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.093436
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.282872
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.047001
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.073738
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.023436
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.016657
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.366346
## Warning in dpois(y, mu, log = TRUE): non-integer x = 6.206400
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.991486
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.009132
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.009132
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.030436
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.023235
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.040873
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.019783
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.007201
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.479587
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.044637
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.767318
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.046738
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.855530
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.499406
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.499406
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.855530
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.938688
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.963580
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.247830
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.978993
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.938688
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.963580
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.257476
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.160672
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.093436
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.282872
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.047001
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.073738
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.023436
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.016657
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.366346
## Warning in dpois(y, mu, log = TRUE): non-integer x = 6.206400
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.991486
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.009132
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.009132
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.030436
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.023235
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.040873
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.019783
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.007201
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.479587
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.044637
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.767318
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.046738
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.855530
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.499406
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.499406
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.855530
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.938688
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.963580
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.247830
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.978993
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.938688
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.963580
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.257476
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.160672
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.093436
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.282872
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.047001
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.073738
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.023436
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.016657
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.366346
## Warning in dpois(y, mu, log = TRUE): non-integer x = 6.206400
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.991486
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.009132
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.009132
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.030436
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.023235
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.040873
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.019783
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.007201
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.479587
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.044637
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.767318
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.046738
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.855530
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.499406
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.499406
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.855530
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.938688
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.963580
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.247830
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.978993
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.938688
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.963580
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.257476
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.160672
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.093436
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.282872
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.047001
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.073738
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.023436
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.016657
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.366346
## Warning in dpois(y, mu, log = TRUE): non-integer x = 6.206400
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.991486
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.009132
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.009132
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.030436
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.023235
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.040873
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.019783
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.007201
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.479587
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.044637
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.767318
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.046738
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.855530
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.499406
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.499406
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.855530
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.938688
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.963580
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.247830
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.978993
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.938688
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.963580
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.257476
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.160672
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.093436
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.282872
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.047001
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.073738
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.023436
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.016657
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.366346
## Warning in dpois(y, mu, log = TRUE): non-integer x = 6.206400
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.991486
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.009132
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.009132
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.030436
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.023235
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.040873
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.019783
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.007201
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.479587
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.044637
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.767318
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.046738
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.855530
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.499406
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.499406
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.855530
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.938688
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.963580
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.247830
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.978993
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.938688
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.963580
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.257476
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.160672
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.093436
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.282872
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.047001
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.073738
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.023436
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.016657
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.366346
## Warning in dpois(y, mu, log = TRUE): non-integer x = 6.206400
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.991486
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.009132
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.009132
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.030436
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.023235
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.040873
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.019783
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.007201
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.479587
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.044637
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.767318
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.046738
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.855530
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.499406
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.499406
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.855530
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.938688
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.963580
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.247830
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.978993
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.938688
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.963580
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.257476
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.160672
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.093436
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.282872
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.047001
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.073738
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.023436
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.016657
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.366346
## Warning in dpois(y, mu, log = TRUE): non-integer x = 6.206400
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.991486
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.009132
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.009132
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.030436
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.023235
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.040873
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.019783
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.007201
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.479587
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.044637
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.767318
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.046738
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.855530
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.499406
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.499406
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.855530
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.938688
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.963580
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.247830
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.978993
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.938688
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.963580
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.257476
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.160672
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.093436
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.282872
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.047001
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.073738
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.023436
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.016657
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.366346
## Warning in dpois(y, mu, log = TRUE): non-integer x = 6.206400
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.991486
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.009132
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.009132
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.030436
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.023235
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.040873
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.019783
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.007201
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.479587
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.044637
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.767318
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.046738
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.855530
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.499406
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.499406
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.855530
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.938688
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.963580
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.247830
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.978993
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.938688
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.963580
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.257476
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.160672
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.093436
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.282872
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.047001
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.073738
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.023436
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.016657
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.366346
## Warning in dpois(y, mu, log = TRUE): non-integer x = 6.206400
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.991486
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.009132
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.009132
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.030436
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.023235
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.040873
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.019783
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.007201
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.479587
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.044637
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.767318
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.046738
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.855530
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.499406
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.499406
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.855530
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.938688
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.963580
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.247830
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.978993
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.938688
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.963580
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.257476
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.160672
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.093436
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.282872
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.047001
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.073738
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.023436
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.016657
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.366346
## Warning in dpois(y, mu, log = TRUE): non-integer x = 6.206400
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.991486
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.009132
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.009132
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.030436
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.023235
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.040873
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.019783
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.007201
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.479587
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.044637
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.767318
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.046738
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.855530
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.499406
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.499406
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.855530
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.938688
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.963580
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.247830
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.978993
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.938688
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.963580
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.257476
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.160672
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.093436
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.282872
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.047001
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.073738
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.023436
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.016657
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.366346
## Warning in dpois(y, mu, log = TRUE): non-integer x = 6.206400
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.991486
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.009132
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.009132
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.030436
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.023235
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.040873
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.019783
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.007201
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.479587
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.044637
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.767318
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.046738
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.855530
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.499406
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.499406
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.855530
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.938688
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.963580
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.247830
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.978993
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.938688
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.963580
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.257476
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.160672
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.093436
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.282872
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.047001
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.073738
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.023436
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.016657
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.366346
## Warning in dpois(y, mu, log = TRUE): non-integer x = 6.206400
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.991486
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.009132
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.009132
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.030436
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.023235
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.040873
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.019783
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.007201
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.479587
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.044637
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.767318
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.046738
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.855530
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.499406
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.499406
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.855530
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.938688
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.963580
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.247830
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.978993
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.938688
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.963580
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.257476
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.160672
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.093436
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.282872
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.047001
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.073738
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.023436
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.016657
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.366346
## Warning in dpois(y, mu, log = TRUE): non-integer x = 6.206400
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.991486
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.009132
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.009132
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.030436
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.023235
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.040873
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.019783
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.007201
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.479587
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.044637
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.767318
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.046738
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.855530
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.499406
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.499406
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.855530
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.938688
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.963580
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.247830
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.978993
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.938688
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.963580
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.257476
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.160672
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.093436
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.282872
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.047001
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.073738
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.023436
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.016657
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.366346
## Warning in dpois(y, mu, log = TRUE): non-integer x = 6.206400
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.991486
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.009132
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.009132
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.030436
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.023235
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.040873
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.019783
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.007201
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.479587
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.044637
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.767318
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.046738
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.855530
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.499406
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.499406
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.855530
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.938688
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.963580
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.247830
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.978993
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.938688
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.963580
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.257476
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.160672
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.093436
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.282872
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.047001
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.073738
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.023436
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.016657
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.366346
## Warning in dpois(y, mu, log = TRUE): non-integer x = 6.206400
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.991486
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.009132
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.009132
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.030436
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.023235
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.040873
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.019783
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.007201
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.479587
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.044637
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.767318
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.046738
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.855530
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.499406
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.499406
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.855530
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.938688
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.963580
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.247830
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.978993
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.695667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.231493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.938688
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.963580
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.257476
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.160672
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.093436
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.282872
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.047001
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.073738
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.023436
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.016657
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.366346
## Warning in dpois(y, mu, log = TRUE): non-integer x = 6.206400
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.991486
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.009132
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.009132
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.030436
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.023235
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.040873
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.019783
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.007201
## Analysis of Deviance Table
##
## Model: poisson, link: log
##
## Response: (Odonata_bio/padj)
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev
## NULL 59 167.881
## maxvol 1 5.717 58 162.165
## site 1 83.022 57 79.142
## mu.scalar 1 3.074 56 76.068
## I(mu.scalar^2) 1 4.696 55 71.372
## k.scalar 1 0.002 54 71.370
## I(k.scalar^2) 1 0.832 53 70.538
## site:mu.scalar 1 0.154 52 70.384
## site:I(mu.scalar^2) 1 8.614 51 61.770
## site:k.scalar 1 2.050 50 59.721
## site:I(k.scalar^2) 1 9.237 49 50.483
## mu.scalar:k.scalar 1 3.087 48 47.396
## mu.scalar:I(k.scalar^2) 1 3.274 47 44.122
## I(mu.scalar^2):k.scalar 1 0.002 46 44.119
## I(mu.scalar^2):I(k.scalar^2) 1 1.911 45 42.209
## site:mu.scalar:k.scalar 1 0.478 44 41.730
## site:mu.scalar:I(k.scalar^2) 1 0.695 43 41.035
## site:I(mu.scalar^2):k.scalar 1 0.002 42 41.033
## site:I(mu.scalar^2):I(k.scalar^2) 1 0.033 41 41.000
## Pr(>Chi)
## NULL
## maxvol 0.016804 *
## site < 2.2e-16 ***
## mu.scalar 0.079541 .
## I(mu.scalar^2) 0.030238 *
## k.scalar 0.964936
## I(k.scalar^2) 0.361748
## site:mu.scalar 0.694466
## site:I(mu.scalar^2) 0.003336 **
## site:k.scalar 0.152224
## site:I(k.scalar^2) 0.002371 **
## mu.scalar:k.scalar 0.078913 .
## mu.scalar:I(k.scalar^2) 0.070364 .
## I(mu.scalar^2):k.scalar 0.961418
## I(mu.scalar^2):I(k.scalar^2) 0.166874
## site:mu.scalar:k.scalar 0.489253
## site:mu.scalar:I(k.scalar^2) 0.404368
## site:I(mu.scalar^2):k.scalar 0.963840
## site:I(mu.scalar^2):I(k.scalar^2) 0.855768
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
m25a<-glm(Corethrellidae_bio~maxvol+site*(mu.scalar+I(mu.scalar^2))*(k.scalar+I(k.scalar^2)), family=poisson, data=cafgmadata);
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.571429
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.785714
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.857143
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.785714
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.428571
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.571429
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.285714
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.571429
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.357143
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.214286
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.285714
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.214286
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.857143
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.571429
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.214286
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.071429
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.214286
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.357143
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.071429
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.428571
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.071429
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.785714
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.571429
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.071429
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.142857
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.428571
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.500000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.357143
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.142857
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.063000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.126000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.063000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.220500
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.126000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.157500
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.094500
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.094500
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.409500
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.063000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.094500
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.157500
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.157500
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.063000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.031500
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.094500
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.063000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.189000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.031500
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.346500
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.063000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.031500
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.031500
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.063000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.424486
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.905146
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.225984
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.378291
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.749677
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.493637
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.094596
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.411418
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.638729
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.460771
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.332654
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.775923
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.582209
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.713403
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.583003
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.484749
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.433226
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.095546
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.644349
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.129676
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.625039
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.451262
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.450154
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.482642
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.547003
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.439855
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.958810
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.397578
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.068565
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.055545
padj<-m25a$deviance/m25a$df.residual
m25g<-glm((Corethrellidae_bio/padj)~maxvol+site*(mu.scalar+I(mu.scalar^2))*(k.scalar+I(k.scalar^2)), family=poisson, data=cafgmadata);
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.231149
## Warning in dpois(y, mu, log = TRUE): non-integer x = 10.876850
## Warning in dpois(y, mu, log = TRUE): non-integer x = 11.155743
## Warning in dpois(y, mu, log = TRUE): non-integer x = 10.876850
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.673361
## Warning in dpois(y, mu, log = TRUE): non-integer x = 7.809020
## Warning in dpois(y, mu, log = TRUE): non-integer x = 6.135659
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.115574
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.231149
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.298978
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.741191
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.115574
## Warning in dpois(y, mu, log = TRUE): non-integer x = 16.454721
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.346723
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.231149
## Warning in dpois(y, mu, log = TRUE): non-integer x = 8.645701
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.183404
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.741191
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.394468
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.278894
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.577872
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.183404
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.067829
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.231149
## Warning in dpois(y, mu, log = TRUE): non-integer x = 11.992424
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.557787
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.577872
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.856765
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.298978
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.462297
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.245984
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.491968
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.245984
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.860944
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.491968
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.614960
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.368976
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.368976
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.598897
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.245984
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.368976
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.614960
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.614960
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.245984
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.122992
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.368976
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.245984
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.737952
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.122992
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.352913
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.245984
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.122992
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.122992
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.245984
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.657410
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.534151
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.786866
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.381552
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.927123
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.927409
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.273862
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.606385
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.493924
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.799087
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.298849
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.029598
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.273239
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.785490
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.276339
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.892708
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.691534
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.277570
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.515865
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.506323
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.440472
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.761956
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.757630
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.884479
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.135780
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.717417
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.743683
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.552345
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.267714
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.216877
visreg(m25g, "mu.scalar", by="site", overlay=FALSE, partial=FALSE, ylab="corethrellid biomass")
visreg(m25g, "k.scalar", by="site", overlay=FALSE, partial=FALSE, ylab="corethrellid biomass")
par(mfrow = c(1, 2))
visreg(m25g, "mu.scalar", by="site", overlay=TRUE, partial=FALSE, ylab="corethrellid biomass")
visreg(m25g, "k.scalar", by="site", overlay=TRUE, partial=FALSE, ylab="corethrellid biomass")
tapply(fulldata$Coleoptera_bio, fulldata$site, datacheck)
## argentina cardoso colombia costarica frenchguiana
## 28 30 29 29 30
## macae puertorico
## 30 29
allmodel(2,fulldata$Coleoptera_bio,poisson, fulldata)#strong conting
## Error in eval(expr, envir, enclos): could not find function "allmodel"
allmodel(2,fulldata$Diptera_bio,poisson, fulldata)#no conting, I think because tipulids are obscuring smaller larvae
## Error in eval(expr, envir, enclos): could not find function "allmodel"
tapply(fulldata$Chironomidae_bio, fulldata$site, datacheck)#really, NO chiron in FG?
## argentina cardoso colombia costarica frenchguiana
## 28 30 30 29 0
## macae puertorico
## 30 15
tapply(fulldata$Orthocladiinae_bio, fulldata$site, datacheck)#mainly Cardoso and Macae
## argentina cardoso colombia costarica frenchguiana
## 0 30 0 2 0
## macae puertorico
## 17 0
tapply(fulldata$Tanypodinae_bio, fulldata$site, datacheck)#mainly Cardoso and Macae
## argentina cardoso colombia costarica frenchguiana
## 0 30 0 0 0
## macae puertorico
## 23 8
#note to self, when have genus level biomass, compare Polypedilum, Monopelopia and Chironomus
tapply(fulldata$Naididae_bio, fulldata$site, datacheck)#common in CA, CO, CR, FG, MA
## argentina cardoso colombia costarica frenchguiana
## 0 28 0 29 23
## macae puertorico
## 30 0
gath.out<-data.frame(full.d2=numeric(5),noint.d2=numeric(5), intend.d2=numeric(5), site.d2=numeric(5), conting.p=numeric(5),general.p=numeric(5), siterain.p=numeric(5), rainfall.p=numeric(5))
gath.out[1,]<-allmodel(2,nofgdata$Chironomidae_bio,poisson, nofgdata)#strong conting
## Error in eval(expr, envir, enclos): could not find function "allmodel"
gath.out[2,]<-allmodel(2,noargprdata$Naididae_bio,poisson, noargprdata)
## Error in eval(expr, envir, enclos): could not find function "allmodel"
gath.out[3,]<-allmodel(2,nofgdata$Tanypodinae_bio,poisson, nofgdata)
## Error in eval(expr, envir, enclos): could not find function "allmodel"
gath.out[4,]<-allmodel(2,nofgdata$Orthocladiinae_bio,poisson, nofgdata)
## Error in eval(expr, envir, enclos): could not find function "allmodel"
gath.out[5,]<-allmodel(2,nofgdata$Chironominae_bio,poisson, nofgdata)
## Error in eval(expr, envir, enclos): could not find function "allmodel"
row.names(gath.out)<-c("Chironomidae biomass","Naididae biomass", "Tanypodinae biomass", "Orthocladinae biomass", "Chironominae biomass")#no conting, effect of rainfall and site
kable(gath.out[,c(1,5,8)])
|
full.d2 |
conting.p |
rainfall.p |
| Chironomidae biomass |
0 |
0 |
0 |
| Naididae biomass |
0 |
0 |
0 |
| Tanypodinae biomass |
0 |
0 |
0 |
| Orthocladinae biomass |
0 |
0 |
0 |
| Chironominae biomass |
0 |
0 |
0 |
m25a<-glm(Chironomidae_bio~maxvol+site*(mu.scalar+I(mu.scalar^2))*(k.scalar+I(k.scalar^2)), family=poisson, data=nofgdata);
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.388889
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.611111
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.388889
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.388889
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.666667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.111111
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.224000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.090385
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.362500
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.166667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.700000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.232000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.166667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.944444
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.750000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.100000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.361538
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.085714
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.666667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.611111
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.777778
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.500000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.773684
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.805000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.555556
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.257143
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.583333
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.075000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 9.360920
## Warning in dpois(y, mu, log = TRUE): non-integer x = 16.230076
## Warning in dpois(y, mu, log = TRUE): non-integer x = 8.495433
## Warning in dpois(y, mu, log = TRUE): non-integer x = 13.768939
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.532208
## Warning in dpois(y, mu, log = TRUE): non-integer x = 6.993929
## Warning in dpois(y, mu, log = TRUE): non-integer x = 20.578047
## Warning in dpois(y, mu, log = TRUE): non-integer x = 8.298690
## Warning in dpois(y, mu, log = TRUE): non-integer x = 10.877187
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.834556
## Warning in dpois(y, mu, log = TRUE): non-integer x = 6.743415
## Warning in dpois(y, mu, log = TRUE): non-integer x = 7.563620
## Warning in dpois(y, mu, log = TRUE): non-integer x = 6.677327
## Warning in dpois(y, mu, log = TRUE): non-integer x = 7.622809
## Warning in dpois(y, mu, log = TRUE): non-integer x = 6.590898
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.365312
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.544043
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.360898
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.852270
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.320574
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.375265
## Warning in dpois(y, mu, log = TRUE): non-integer x = 13.461245
## Warning in dpois(y, mu, log = TRUE): non-integer x = 12.327468
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.784394
## Warning in dpois(y, mu, log = TRUE): non-integer x = 7.484426
## Warning in dpois(y, mu, log = TRUE): non-integer x = 9.614557
## Warning in dpois(y, mu, log = TRUE): non-integer x = 15.285250
## Warning in dpois(y, mu, log = TRUE): non-integer x = 9.780768
## Warning in dpois(y, mu, log = TRUE): non-integer x = 9.270422
## Warning in dpois(y, mu, log = TRUE): non-integer x = 13.190000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.921251
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.291357
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.448410
## Warning in dpois(y, mu, log = TRUE): non-integer x = 9.822566
## Warning in dpois(y, mu, log = TRUE): non-integer x = 6.518016
## Warning in dpois(y, mu, log = TRUE): non-integer x = 6.613320
## Warning in dpois(y, mu, log = TRUE): non-integer x = 58.685394
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.390051
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.262876
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.934369
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.625019
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.315216
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.385289
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.719507
## Warning in dpois(y, mu, log = TRUE): non-integer x = 6.562165
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.060579
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.065656
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.166393
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.482093
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.222719
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.838174
## Warning in dpois(y, mu, log = TRUE): non-integer x = 10.202464
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.918019
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.541719
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.017600
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.335443
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.176681
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.466699
## Warning in dpois(y, mu, log = TRUE): non-integer x = 6.676074
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.824647
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.906611
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.639165
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.259721
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.226569
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.579485
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.056284
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.454592
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.450652
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.959959
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.471189
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.162974
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.536220
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.641135
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.035068
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.599201
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.026604
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.594771
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.697489
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.635521
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.975328
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.653297
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.830894
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.978383
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.007216
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.818861
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.365594
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.253689
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.706056
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.988682
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.482806
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.888700
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.493300
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.389600
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.149300
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.365400
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.209500
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.318900
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.473200
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.087000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.149300
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.174600
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.087300
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.193000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.043700
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.149300
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.210000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.735000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.035000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.175000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.041684
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.203000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.175000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.273000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.105000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.140000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.470000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.470000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.295000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.015000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.463000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.826000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.245000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.210000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.105000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.350000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.980000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.015000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.350000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.756000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.840000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.175000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.350000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.385000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.420000
padj<-m25a$deviance/m25a$df.residual
m25b<-glm((Chironomidae_bio/padj)~maxvol+site*(mu.scalar+I(mu.scalar^2))*(k.scalar+I(k.scalar^2)), family=poisson, data=nofgdata);
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.326031
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.512335
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.326031
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.326031
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.558910
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.093152
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.187794
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.914141
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.303908
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.139728
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.586856
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.032866
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.816459
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.791790
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.628774
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.922202
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.303101
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.071860
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.558910
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.512335
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.652062
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.419183
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.648630
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.674884
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.465759
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.053945
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.489047
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.062877
## Warning in dpois(y, mu, log = TRUE): non-integer x = 7.847873
## Warning in dpois(y, mu, log = TRUE): non-integer x = 13.606736
## Warning in dpois(y, mu, log = TRUE): non-integer x = 7.122278
## Warning in dpois(y, mu, log = TRUE): non-integer x = 11.543404
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.638012
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.863469
## Warning in dpois(y, mu, log = TRUE): non-integer x = 17.251926
## Warning in dpois(y, mu, log = TRUE): non-integer x = 6.957336
## Warning in dpois(y, mu, log = TRUE): non-integer x = 9.119059
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.376394
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.653447
## Warning in dpois(y, mu, log = TRUE): non-integer x = 6.341079
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.598041
## Warning in dpois(y, mu, log = TRUE): non-integer x = 6.390700
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.525582
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.821362
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.971204
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.817661
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.067976
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.945489
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.506437
## Warning in dpois(y, mu, log = TRUE): non-integer x = 11.285444
## Warning in dpois(y, mu, log = TRUE): non-integer x = 10.334924
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.849437
## Warning in dpois(y, mu, log = TRUE): non-integer x = 6.274685
## Warning in dpois(y, mu, log = TRUE): non-integer x = 8.060513
## Warning in dpois(y, mu, log = TRUE): non-integer x = 12.814627
## Warning in dpois(y, mu, log = TRUE): non-integer x = 8.199859
## Warning in dpois(y, mu, log = TRUE): non-integer x = 7.772002
## Warning in dpois(y, mu, log = TRUE): non-integer x = 11.058042
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.125807
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.244263
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.375932
## Warning in dpois(y, mu, log = TRUE): non-integer x = 8.234901
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.464480
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.544380
## Warning in dpois(y, mu, log = TRUE): non-integer x = 49.199812
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.518833
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.058752
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.783343
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.200725
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.779363
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.323013
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.956672
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.501493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.565884
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.055044
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.977863
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.919267
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.186720
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.379427
## Warning in dpois(y, mu, log = TRUE): non-integer x = 8.553395
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.446366
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.292524
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.853121
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.119589
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.986488
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.067996
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.596991
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.691356
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.760072
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.535854
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.732838
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.028313
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.162551
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.562283
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.219480
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.377811
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.643162
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.071760
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.974997
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.449548
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.214237
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.706131
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.340715
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.860669
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.013732
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.584750
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.371165
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.656047
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.547702
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.373324
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.658608
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.844415
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.686505
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.144867
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.889415
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.430299
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.828877
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.243133
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.745055
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.413566
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.164993
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.125168
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.306339
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.014003
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.267355
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.396715
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.072938
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.125168
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.146379
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.073189
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.161805
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.036637
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.125168
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.176057
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.616199
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.029343
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.146714
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.873312
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.170188
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.146714
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.228874
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.088028
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.117371
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.232397
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.232397
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.085683
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.850941
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.226529
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.692490
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.205400
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.176057
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.088028
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.293428
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.821598
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.850941
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.293428
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.633804
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.704227
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.146714
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.293428
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.322771
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.352114
visreg(m25b, "mu.scalar", by="site", overlay=FALSE, partial=FALSE, ylab="Chironomid biomass")
visreg(m25b, "k.scalar", by="site", overlay=FALSE, partial=FALSE, ylab="Chironomid biomass")
m25a<-glm(gatherer_bio~maxvol+site*(mu.scalar+I(mu.scalar^2))*(k.scalar+I(k.scalar^2)), family=poisson, data=no67185data);
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.388889
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.611111
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.388889
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.288889
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.666667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.111111
## Warning in dpois(y, mu, log = TRUE): non-integer x = 8.409714
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.235714
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.390385
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.562500
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.166667
## Warning in dpois(y, mu, log = TRUE): non-integer x = 8.078571
## Warning in dpois(y, mu, log = TRUE): non-integer x = 40.903429
## Warning in dpois(y, mu, log = TRUE): non-integer x = 66.880952
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.415873
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.750000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.597253
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.085714
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.138095
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.611111
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.013492
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.207143
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.773684
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.512143
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.555556
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.257143
## Warning in dpois(y, mu, log = TRUE): non-integer x = 11.554762
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.675000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 12.450200
## Warning in dpois(y, mu, log = TRUE): non-integer x = 15.965676
## Warning in dpois(y, mu, log = TRUE): non-integer x = 8.937647
## Warning in dpois(y, mu, log = TRUE): non-integer x = 14.578024
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.322072
## Warning in dpois(y, mu, log = TRUE): non-integer x = 6.721565
## Warning in dpois(y, mu, log = TRUE): non-integer x = 19.823362
## Warning in dpois(y, mu, log = TRUE): non-integer x = 7.864799
## Warning in dpois(y, mu, log = TRUE): non-integer x = 24.598631
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.286554
## Warning in dpois(y, mu, log = TRUE): non-integer x = 6.873044
## Warning in dpois(y, mu, log = TRUE): non-integer x = 34.568280
## Warning in dpois(y, mu, log = TRUE): non-integer x = 7.667707
## Warning in dpois(y, mu, log = TRUE): non-integer x = 8.462409
## Warning in dpois(y, mu, log = TRUE): non-integer x = 6.280541
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.895799
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.758292
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.502328
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.113461
## Warning in dpois(y, mu, log = TRUE): non-integer x = 28.656936
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.088427
## Warning in dpois(y, mu, log = TRUE): non-integer x = 12.723519
## Warning in dpois(y, mu, log = TRUE): non-integer x = 25.554352
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.977582
## Warning in dpois(y, mu, log = TRUE): non-integer x = 7.092119
## Warning in dpois(y, mu, log = TRUE): non-integer x = 35.801199
## Warning in dpois(y, mu, log = TRUE): non-integer x = 14.441229
## Warning in dpois(y, mu, log = TRUE): non-integer x = 22.013119
## Warning in dpois(y, mu, log = TRUE): non-integer x = 9.814957
## Warning in dpois(y, mu, log = TRUE): non-integer x = 13.008869
## Warning in dpois(y, mu, log = TRUE): non-integer x = 13.611851
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.342557
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.172766
## Warning in dpois(y, mu, log = TRUE): non-integer x = 13.511366
## Warning in dpois(y, mu, log = TRUE): non-integer x = 12.187016
## Warning in dpois(y, mu, log = TRUE): non-integer x = 6.627620
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.432951
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.286176
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.934369
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.327991
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.422816
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.395852
## Warning in dpois(y, mu, log = TRUE): non-integer x = 6.217110
## Warning in dpois(y, mu, log = TRUE): non-integer x = 10.926265
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.182479
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.131156
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.259693
## Warning in dpois(y, mu, log = TRUE): non-integer x = 12.158793
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.222719
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.852474
## Warning in dpois(y, mu, log = TRUE): non-integer x = 10.295764
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.312219
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.627519
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.017600
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.222443
## Warning in dpois(y, mu, log = TRUE): non-integer x = 28.274907
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.681199
## Warning in dpois(y, mu, log = TRUE): non-integer x = 16.501164
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.890147
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.130000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.120000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.120000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.150000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.030000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.060000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.180000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.030000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.060000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.550000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.060000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.120000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.150000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.150000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.100000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.030000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.090000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.120000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.730000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.090000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.330000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.150000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.150000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 7.017654
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.331563
## Warning in dpois(y, mu, log = TRUE): non-integer x = 6.049389
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.738901
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.156570
## Warning in dpois(y, mu, log = TRUE): non-integer x = 11.279469
## Warning in dpois(y, mu, log = TRUE): non-integer x = 9.350163
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.311747
## Warning in dpois(y, mu, log = TRUE): non-integer x = 6.900546
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.517614
## Warning in dpois(y, mu, log = TRUE): non-integer x = 6.925756
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.532338
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.600917
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.919053
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.379237
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.837776
## Warning in dpois(y, mu, log = TRUE): non-integer x = 6.985727
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.350173
## Warning in dpois(y, mu, log = TRUE): non-integer x = 15.090893
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.846235
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.515616
## Warning in dpois(y, mu, log = TRUE): non-integer x = 7.061544
## Warning in dpois(y, mu, log = TRUE): non-integer x = 6.566711
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.894349
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.267597
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.689903
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.353487
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.728354
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.225719
## Warning in dpois(y, mu, log = TRUE): non-integer x = 10.051474
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.292500
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.702200
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.141300
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.389600
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.547300
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.585000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.672500
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.254300
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.325000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.151600
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.560000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.524100
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.385900
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.552500
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.087000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.612300
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.257300
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.162500
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.292500
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.463000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.798700
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.162500
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.368700
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.149300
## Warning in dpois(y, mu, log = TRUE): non-integer x = 12.130000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.775000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.515000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 15.715000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.943000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 14.692270
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.493000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 9.932270
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.640000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 42.927270
## Warning in dpois(y, mu, log = TRUE): non-integer x = 14.477270
## Warning in dpois(y, mu, log = TRUE): non-integer x = 17.072270
## Warning in dpois(y, mu, log = TRUE): non-integer x = 9.895000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 20.390270
## Warning in dpois(y, mu, log = TRUE): non-integer x = 13.613270
## Warning in dpois(y, mu, log = TRUE): non-integer x = 13.772270
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.170000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 12.694540
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.545000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 12.190000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.230000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 9.895000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 25.484540
## Warning in dpois(y, mu, log = TRUE): non-integer x = 29.760540
## Warning in dpois(y, mu, log = TRUE): non-integer x = 6.840000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.135000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 7.010000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.125000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 13.237270
padj<-m25a$deviance/m25a$df.residual
m25b<-glm((gatherer_bio/padj)~maxvol+site*(mu.scalar+I(mu.scalar^2))*(k.scalar+I(k.scalar^2)), family=poisson, data=no67185data);
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.092638
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.145575
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.092638
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.783456
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.158809
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.026468
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.003304
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.056150
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.569421
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.133995
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.039702
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.924421
## Warning in dpois(y, mu, log = TRUE): non-integer x = 9.743732
## Warning in dpois(y, mu, log = TRUE): non-integer x = 15.931919
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.337279
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.178660
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.667492
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.142273
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.020418
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.271109
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.145575
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.241427
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.287557
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.184302
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.360212
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.132341
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.299468
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.752496
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.637220
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.965801
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.803233
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.129064
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.472676
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.267787
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.601165
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.722184
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.873498
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.859716
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.782900
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.637249
## Warning in dpois(y, mu, log = TRUE): non-integer x = 8.234617
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.826548
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.015857
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.496107
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.689817
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.895274
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.834300
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.218093
## Warning in dpois(y, mu, log = TRUE): non-integer x = 6.826457
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.212130
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.030909
## Warning in dpois(y, mu, log = TRUE): non-integer x = 6.087381
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.185725
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.689436
## Warning in dpois(y, mu, log = TRUE): non-integer x = 8.528314
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.440090
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.243813
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.338051
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.098883
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.242521
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.081601
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.279368
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.218584
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.903107
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.578786
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.294200
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.544597
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.222579
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.030984
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.815360
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.570723
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.480997
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.602779
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.758108
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.031243
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.300075
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.896384
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.053055
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.679497
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.452586
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.789014
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.387696
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.242406
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.005841
## Warning in dpois(y, mu, log = TRUE): non-integer x = 6.735453
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.638697
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.930793
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.212045
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.030968
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.028586
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.028586
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.035732
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.007146
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.014293
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.042878
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.007146
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.014293
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.131017
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.014293
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.028586
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.035732
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.035732
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.023821
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.007146
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.021439
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.028586
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.173896
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.021439
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.078610
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.035732
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.035732
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.671697
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.270048
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.441044
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.890655
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.513723
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.686917
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.227331
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.788901
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.643801
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.314368
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.649806
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.365023
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.334212
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.695357
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.804978
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.437782
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.664092
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.036268
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.594848
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.154437
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.075679
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.682152
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.564277
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.927685
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.301958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.402557
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.275271
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.888143
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.006622
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.394393
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.069677
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.167273
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.986512
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.331021
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.368587
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.139355
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.874838
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.298791
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.077419
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.274326
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.133399
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.124847
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.091926
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.131613
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.020725
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.145858
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.299505
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.038710
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.069677
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.110293
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.428474
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.038710
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.087829
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.035565
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.889525
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.899254
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.360893
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.743519
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.224635
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.499891
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.308505
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.365997
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.105309
## Warning in dpois(y, mu, log = TRUE): non-integer x = 10.225838
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.448675
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.066838
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.357119
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.857229
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.242859
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.280735
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.755136
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.024006
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.082679
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.903818
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.769428
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.357119
## Warning in dpois(y, mu, log = TRUE): non-integer x = 6.070751
## Warning in dpois(y, mu, log = TRUE): non-integer x = 7.089350
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.629378
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.746798
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.669874
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.267990
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.153291
visreg(m25b, "mu.scalar", by="site", overlay=FALSE, partial=FALSE, ylab="Gatherer biomass")
visreg(m25b, "k.scalar", by="site", overlay=FALSE, partial=FALSE, ylab="Gatherer biomass")
tapply(fulldata$Culicidae_bio, fulldata$site, datacheck)#all sites but rare in CR CO
## argentina cardoso colombia costarica frenchguiana
## 14 29 5 9 29
## macae puertorico
## 30 24
tapply(fulldata$Culex, fulldata$site, datacheck)#all but Colombia
## argentina cardoso colombia costarica frenchguiana
## 14 29 0 2 21
## macae puertorico
## 29 22
tapply(fulldata$Wyeomyia, fulldata$site, datacheck)#not arg, col, and only 2 in cardoso
## argentina cardoso colombia costarica frenchguiana
## 0 2 0 9 21
## macae puertorico
## 19 17
tapply(fulldata$Anopheles, fulldata$site, datacheck)#only cardoso (9) and fg (4)
## argentina cardoso colombia costarica frenchguiana
## 0 9 0 0 4
## macae puertorico
## 0 0
allmodel(2,fulldata$Culicidae_bio,poisson, fulldata)#strong conting
## Error in eval(expr, envir, enclos): could not find function "allmodel"
allmodel(2,nococrdata$Culicidae_bio,poisson, nococrdata)#still strong conting
## Error in eval(expr, envir, enclos): could not find function "allmodel"
#note to self, when have genus level biomass, compare Culex and Wyeomyia
culic.out<-data.frame(full.d2=numeric(3),noint.d2=numeric(3), intend.d2=numeric(3), site.d2=numeric(3), conting.p=numeric(3),general.p=numeric(3), siterain.p=numeric(3), rainfall.p=numeric(3))
culic.out[1,]<-allmodel(2,fulldata$Culicidae_bio,poisson, fulldata)
## Error in eval(expr, envir, enclos): could not find function "allmodel"
culic.out[1,]<-allmodel(2,fulldata$Culex,poisson, fulldata)
## Error in eval(expr, envir, enclos): could not find function "allmodel"
culic.out[1,]<-allmodel(2,fulldata$Wyeomyia,poisson, fulldata)
## Error in eval(expr, envir, enclos): could not find function "allmodel"
row.names(culic.out)<-c("Culicid biomass", "Culex","Wyeomyia")
kable(culic.out[,c(1,5)]) #Culex and Wyeomyia still show strong conting
|
full.d2 |
conting.p |
| Culicid biomass |
0 |
0 |
| Culex |
0 |
0 |
| Wyeomyia |
0 |
0 |
m25a<-glm(Culicidae_bio~maxvol+site*(mu.scalar+I(mu.scalar^2))*(k.scalar+I(k.scalar^2)), family=poisson, data=nococrdata);
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.247059
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.082353
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.329412
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.228571
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.082353
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.247059
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.494118
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.576471
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.494118
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.823529
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.244444
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.411765
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.411765
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.247059
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.562500
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.493750
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.742647
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.365074
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.018750
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.197794
## Warning in dpois(y, mu, log = TRUE): non-integer x = 6.690074
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.662500
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.475000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.356250
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.306250
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.087500
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.415074
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.455147
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.475000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.781250
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.336397
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.325000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.030147
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.662500
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.612500
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.775000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.118750
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.325000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.800000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.169118
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.475000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.475000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.256250
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.091750
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.129460
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.037700
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.581900
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.631000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.258900
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.366800
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.140500
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.015700
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.214900
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.214400
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.169400
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.061000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.076700
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.125600
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.790900
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.426800
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.598900
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.810300
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.243800
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.337500
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.656400
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.667500
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.385100
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.113100
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.062800
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.129400
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.098700
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.015700
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.864235
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.793882
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.251240
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.820602
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.219324
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.004867
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.959077
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.372811
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.571680
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.843340
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.724808
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.188191
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.607987
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.717818
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.462076
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.192289
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.678217
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.193536
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.054319
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.812373
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.889483
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.998089
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.512412
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.570749
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.312998
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.169870
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.685472
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.850012
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.552398
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.423732
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.604700
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.569100
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.035300
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.148000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.258800
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.779600
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.865900
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.693500
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.573400
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.949300
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.886400
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.797300
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.080200
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.149900
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.928500
## Warning in dpois(y, mu, log = TRUE): non-integer x = 9.455400
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.173100
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.855900
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.395600
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.836800
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.940500
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.128400
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.186200
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.240000
padj<-m25a$deviance/m25a$df.residual
m25b<-glm((Culicidae_bio/padj)~maxvol+site*(mu.scalar+I(mu.scalar^2))*(k.scalar+I(k.scalar^2)), family=poisson, data=nococrdata);
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.457575
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.152525
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.610100
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.423335
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.152525
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.457575
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.915150
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.067675
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.915150
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.525250
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.452733
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.762625
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.762625
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.457575
## Warning in dpois(y, mu, log = TRUE): non-integer x = 6.598070
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.618649
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.227539
## Warning in dpois(y, mu, log = TRUE): non-integer x = 6.232419
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.738906
## Warning in dpois(y, mu, log = TRUE): non-integer x = 7.774692
## Warning in dpois(y, mu, log = TRUE): non-integer x = 12.390617
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.079099
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.879743
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.659807
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.419292
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.718328
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.472933
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.547153
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.879743
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.299035
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.327217
## Warning in dpois(y, mu, log = TRUE): non-integer x = 6.158199
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.907925
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.079099
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.838585
## Warning in dpois(y, mu, log = TRUE): non-integer x = 8.843729
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.219936
## Warning in dpois(y, mu, log = TRUE): non-integer x = 6.158199
## Warning in dpois(y, mu, log = TRUE): non-integer x = 7.037942
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.869491
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.879743
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.879743
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.178778
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.169929
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.239772
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.069824
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.077731
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.168669
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.479506
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.531436
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.260219
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.029078
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.398014
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.397088
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.313744
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.112977
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.142055
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.232622
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.464818
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.790472
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.109217
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.500748
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.451540
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.625080
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.215712
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.236270
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.713240
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.209471
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.116311
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.239660
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.182801
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.029078
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.452731
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.470340
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.169498
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.224008
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.258298
## Warning in dpois(y, mu, log = TRUE): non-integer x = 7.417374
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.628386
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.690479
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.762983
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.414031
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.194499
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.052727
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.978136
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.329463
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.559987
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.208227
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.256120
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.210535
## Warning in dpois(y, mu, log = TRUE): non-integer x = 9.361053
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.208768
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.647403
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.700641
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.801123
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.909168
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.283881
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.166704
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.973735
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.426388
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.875180
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.784791
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.972049
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.054024
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.065379
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.274109
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.479321
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.443889
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.603725
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.284424
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.061988
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.758189
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.641692
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.476671
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.148538
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.129718
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.719665
## Warning in dpois(y, mu, log = TRUE): non-integer x = 17.512251
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.320597
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.585204
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.732687
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.401919
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.741891
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.237808
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.344859
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.444502
visreg(m25b, "mu.scalar", by="site", overlay=FALSE, partial=FALSE, ylab="Filter biomass")
visreg(m25b, "k.scalar", by="site", overlay=FALSE, partial=FALSE, ylab="Filter biomass")
tapply(fulldata$Tipulidae_bio, fulldata$site, datacheck)
## argentina cardoso colombia costarica frenchguiana
## 21 5 2 1 0
## macae puertorico
## 30 0
tapply(fulldata$Limoniidae_bio, fulldata$site, datacheck)
## argentina cardoso colombia costarica frenchguiana
## 0 28 16 29 29
## macae puertorico
## 0 29
tapply(fulldata$TipulidaeLimoniidae_bio, fulldata$site, datacheck)#common all bromeliads all sites
## argentina cardoso colombia costarica frenchguiana
## 21 28 18 29 29
## macae puertorico
## 30 29
tip.out<-data.frame(full.d2=numeric(1),noint.d2=numeric(1), intend.d2=numeric(1), site.d2=numeric(1), conting.p=numeric(1),general.p=numeric(1), siterain.p=numeric(1), rainfall.p=numeric(1))
tip.out[1,]<-allmodel(2,fulldata$TipulidaeLimoniidae_bio,poisson, fulldata); row.names(tip.out)<-"Tipulid biomass"
## Error in eval(expr, envir, enclos): could not find function "allmodel"
#no conting or rain effect, only site
#is this part of the reason why litter decomposition not contingent or rainfall sensitive?
kable(tip.out[,c(5,7,8)])
|
conting.p |
siterain.p |
rainfall.p |
| Tipulid biomass |
0 |
0 |
0 |
m25a<-glm(TipulidaeLimoniidae_bio~maxvol+site*(mu.scalar+I(mu.scalar^2))*(k.scalar+I(k.scalar^2)), family=poisson, data=fulldata);
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.300000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.400000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.600000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.800000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.254545
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.200000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.100000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.300000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.509091
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.900000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.300000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.018182
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.300000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.509091
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.900000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.509091
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.300000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.900000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.254545
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.200000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.225263
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.762807
## Warning in dpois(y, mu, log = TRUE): non-integer x = 6.450526
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.612632
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.687719
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.762807
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.687719
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.762807
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.837895
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.687719
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.225263
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.762807
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.075088
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.225263
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.537544
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.837895
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.912982
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.537544
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.837895
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.537544
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.537544
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.912982
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.687719
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.537544
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.537544
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.075088
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.075088
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.912982
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.424497
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.022071
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.220443
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.078264
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.300364
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.175036
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.140291
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.087252
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.036805
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.050845
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.067615
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.185814
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.180425
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.175036
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.277000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.185814
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.466946
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.428956
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.457500
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.572500
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.097500
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.320000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.755000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.115000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.480000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.640000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.115000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.977500
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.955000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.572500
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.390000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.230000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.977500
## Warning in dpois(y, mu, log = TRUE): non-integer x = 11.855000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 7.162500
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.137500
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.617500
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.595000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.057500
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.320000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.115000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.390000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.297500
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.457500
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.847500
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.435000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.710000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.509663
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.909235
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.979131
## Warning in dpois(y, mu, log = TRUE): non-integer x = 6.677545
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.070533
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.966396
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.958793
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.767033
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.489514
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.032919
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.357522
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.117160
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.968122
## Warning in dpois(y, mu, log = TRUE): non-integer x = 6.391080
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.965087
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.441654
## Warning in dpois(y, mu, log = TRUE): non-integer x = 6.424555
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.710759
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.795916
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.518862
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.023136
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.612613
## Warning in dpois(y, mu, log = TRUE): non-integer x = 8.978611
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.095585
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.917305
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.710746
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.461600
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.873181
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.417506
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.030668
## Warning in dpois(y, mu, log = TRUE): non-integer x = 14.339900
## Warning in dpois(y, mu, log = TRUE): non-integer x = 11.434200
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.680300
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.854600
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.834800
## Warning in dpois(y, mu, log = TRUE): non-integer x = 15.581100
## Warning in dpois(y, mu, log = TRUE): non-integer x = 13.315400
## Warning in dpois(y, mu, log = TRUE): non-integer x = 11.362600
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.835300
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.985000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 6.151600
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.633600
## Warning in dpois(y, mu, log = TRUE): non-integer x = 19.123300
## Warning in dpois(y, mu, log = TRUE): non-integer x = 15.593200
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.668200
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.645600
## Warning in dpois(y, mu, log = TRUE): non-integer x = 10.454600
## Warning in dpois(y, mu, log = TRUE): non-integer x = 17.242100
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.736200
## Warning in dpois(y, mu, log = TRUE): non-integer x = 12.854600
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.680300
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.162200
## Warning in dpois(y, mu, log = TRUE): non-integer x = 10.868100
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.668200
## Warning in dpois(y, mu, log = TRUE): non-integer x = 14.385300
## Warning in dpois(y, mu, log = TRUE): non-integer x = 9.604100
## Warning in dpois(y, mu, log = TRUE): non-integer x = 10.654800
## Warning in dpois(y, mu, log = TRUE): non-integer x = 10.850700
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.834800
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.653236
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.975483
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.163043
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.096796
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.736487
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.761407
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.498894
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.901569
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.762762
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.079160
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.397955
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.065515
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.857039
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.244995
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.371372
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.342102
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.256095
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.221626
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.024882
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.443458
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.686265
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.326527
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.554934
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.200799
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.740824
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.529573
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.854425
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.490826
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.113226
padj<-m25a$deviance/m25a$df.residual
m25b<-glm((TipulidaeLimoniidae_bio/padj)~maxvol+site*(mu.scalar+I(mu.scalar^2))*(k.scalar+I(k.scalar^2)), family=poisson, data=fulldata);
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.199073
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.729871
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.312802
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.938405
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.654040
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.104267
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.573470
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.199073
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.308080
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.990539
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.156401
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.616160
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.156401
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.308080
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.469203
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.308080
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.521336
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.156401
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.469203
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.654040
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.146940
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.681446
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.961687
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.362893
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.840723
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.401205
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.961687
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.401205
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.961687
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.522170
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.401205
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.681446
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.961687
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.560482
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.681446
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.280241
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.522170
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.082652
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.280241
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.522170
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.280241
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.280241
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.082652
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.401205
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.280241
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.280241
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.560482
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.560482
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.082652
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.221306
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.011507
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.114925
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.040802
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.156591
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.091252
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.073139
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.045488
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.019188
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.026508
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.035250
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.096871
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.094062
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.091252
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.144410
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.096871
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.243436
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.223630
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.759848
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.862474
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.093503
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.166828
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.436281
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.102626
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.250241
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.333655
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.102626
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.509606
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.019212
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.862474
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.288666
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.205252
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.509606
## Warning in dpois(y, mu, log = TRUE): non-integer x = 6.180441
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.734071
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.593020
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.843261
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.352867
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.593985
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.166828
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.102626
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.288666
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.676434
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.759848
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.048513
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.269454
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.455493
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.265706
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.516689
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.553129
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.481246
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.036772
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.025154
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.542526
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.963891
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.255202
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.059834
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.229061
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.146424
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.547389
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.331901
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.024471
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.836931
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.349353
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.891881
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.414940
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.270502
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.533398
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.362050
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.680875
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.613841
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.563569
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.891874
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.240649
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.455221
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.824342
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.058661
## Warning in dpois(y, mu, log = TRUE): non-integer x = 7.475909
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.961062
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.876001
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.488206
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.999220
## Warning in dpois(y, mu, log = TRUE): non-integer x = 8.122991
## Warning in dpois(y, mu, log = TRUE): non-integer x = 6.941800
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.923735
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.478145
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.556189
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.207052
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.415663
## Warning in dpois(y, mu, log = TRUE): non-integer x = 9.969668
## Warning in dpois(y, mu, log = TRUE): non-integer x = 8.129300
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.348357
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.943256
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.450361
## Warning in dpois(y, mu, log = TRUE): non-integer x = 8.988931
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.990489
## Warning in dpois(y, mu, log = TRUE): non-integer x = 6.701568
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.876001
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.084561
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.665934
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.348357
## Warning in dpois(y, mu, log = TRUE): non-integer x = 7.499578
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.006965
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.554733
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.656863
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.999220
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.861892
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.508555
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.085000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.093136
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.905294
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.960958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.260092
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.470020
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.918992
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.083941
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.250141
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.640836
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.968142
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.649061
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.236282
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.699686
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.218856
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.115542
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.012972
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.795200
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.400447
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.170231
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.810644
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.104684
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.428891
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.318758
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.445443
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.777222
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.623037
visreg(m25b, "mu.scalar", by="site", overlay=FALSE, partial=FALSE, ylab="Tipulid biomass")
visreg(m25b, "k.scalar", by="site", overlay=FALSE, partial=TRUE, ylab="Tipulid biomass")
tapply(fulldata$Trichoptera_bio, fulldata$site, datacheck) #just in 19 cardoso brom
## argentina cardoso colombia costarica frenchguiana
## 0 19 0 0 0
## macae puertorico
## 0 0
m1<-glm(Trichoptera_bio~maxvol+(mu.scalar+I(mu.scalar^2))*(k.scalar+I(k.scalar^2)), family=poisson, data=cadata);
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.250000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.250000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.250000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.250000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.250000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.250000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.250000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.250000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.250000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.500000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.250000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.250000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.500000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.250000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.250000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.250000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.250000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.250000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.250000
padj<-m1$deviance/m1$df.residual
m2<-glm((Trichoptera_bio/padj)~maxvol+(mu.scalar+I(mu.scalar^2))*(k.scalar+I(k.scalar^2)), family=poisson, data=cadata)
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.639915
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.639915
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
Anova(m2,type=3, test.statistic="LR")#Tricoptera in CA has strong effects of mu2xk2, mu x k2, mu x k, k2, k
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.639915
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.639915
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.639915
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.639915
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.639915
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.639915
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.639915
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.639915
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.639915
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.639915
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.639915
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.639915
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.639915
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.639915
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.639915
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.639915
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.639915
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.639915
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Analysis of Deviance Table (Type III tests)
##
## Response: (Trichoptera_bio/padj)
## LR Chisq Df Pr(>Chisq)
## maxvol 0.2765 1 0.599027
## mu.scalar 2.4407 1 0.118224
## I(mu.scalar^2) 1.4206 1 0.233300
## k.scalar 5.7727 1 0.016277 *
## I(k.scalar^2) 6.7167 1 0.009552 **
## mu.scalar:k.scalar 5.0946 1 0.024000 *
## mu.scalar:I(k.scalar^2) 6.3150 1 0.011972 *
## I(mu.scalar^2):k.scalar 3.6834 1 0.054955 .
## I(mu.scalar^2):I(k.scalar^2) 4.8471 1 0.027692 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
m3<-glm((Trichoptera_bio/padj)~maxvol, family=poisson, data=cadata)
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.639915
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.639915
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.819958
anova(m2,m3,test="LRT") #sig effect of rain variables, p=0.027 I think this explains marginal contingency for shredders
## Analysis of Deviance Table
##
## Model 1: (Trichoptera_bio/padj) ~ maxvol + (mu.scalar + I(mu.scalar^2)) *
## (k.scalar + I(k.scalar^2))
## Model 2: (Trichoptera_bio/padj) ~ maxvol
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 20 20.000
## 2 28 37.268 -8 -17.268 0.02743 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
tapply(fulldata$Scirtidae_bio, fulldata$site, datacheck)#in virtually all bromeliads in all sites
## argentina cardoso colombia costarica frenchguiana
## 28 30 29 29 30
## macae puertorico
## 30 29
#this makes sense from Sarah's data: scirtids much more sensitive to drought than tipulids
scirt.out<-data.frame(full.d2=numeric(1),noint.d2=numeric(1), intend.d2=numeric(1), site.d2=numeric(1), conting.p=numeric(1),general.p=numeric(1), siterain.p=numeric(1), rainfall.p=numeric(1))
scirt.out[1,]<-allmodel(2,fulldata$Scirtidae_bio,poisson, fulldata); row.names(scirt.out)<-"Scirtid biomass"
## Error in eval(expr, envir, enclos): could not find function "allmodel"
kable(scirt.out[,c(1,5)])
|
full.d2 |
conting.p |
| Scirtid biomass |
0 |
0 |
#CHECK THIS - aliased coeff in the model!! this seems okay Jan 2017
m24a<-glm(Scirtidae_bio~maxvol+site*(mu.scalar+I(mu.scalar^2))*(k.scalar+I(k.scalar^2)), family=poisson, data=fulldata);
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.105263
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.940909
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.500000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.392857
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.800000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.600000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.500000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.576471
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.700000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.363380
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.160000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.400000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.362500
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.741176
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.121127
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.240000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.250000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.812500
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.800000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.437931
## Warning in dpois(y, mu, log = TRUE): non-integer x = 9.818182
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.242254
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.400000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.242254
## Warning in dpois(y, mu, log = TRUE): non-integer x = 8.138298
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.264516
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.060563
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.921739
## Warning in dpois(y, mu, log = TRUE): non-integer x = 19.350000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 27.900000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 6.750000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 31.500000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 13.050000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 6.750000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 21.150000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 11.250000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 6.300000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 6.300000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 17.100000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 15.300000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 20.700000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 13.050000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 35.550000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 18.900000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 23.850000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 33.300000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 18.450000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 6.750000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 6.300000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 18.900000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 14.850000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 6.300000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 9.450000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 15.300000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 19.350000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 11.700000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 32.400000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 31.955099
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.051897
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.991110
## Warning in dpois(y, mu, log = TRUE): non-integer x = 6.142380
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.405348
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.644406
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.282857
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.058816
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.878305
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.097304
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.212934
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.200124
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.411888
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.054500
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.278775
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.642916
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.275230
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.150889
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.164366
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.021656
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.275732
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.642050
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.976273
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.849865
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.222286
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.763469
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.455360
## Warning in dpois(y, mu, log = TRUE): non-integer x = 8.548035
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.847370
## Warning in dpois(y, mu, log = TRUE): non-integer x = 9.667200
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.953800
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.326300
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.535400
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.654500
## Warning in dpois(y, mu, log = TRUE): non-integer x = 9.578100
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.969500
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.238800
## Warning in dpois(y, mu, log = TRUE): non-integer x = 13.457700
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.251500
## Warning in dpois(y, mu, log = TRUE): non-integer x = 10.085100
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.445700
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.132900
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.714200
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.132100
## Warning in dpois(y, mu, log = TRUE): non-integer x = 15.963000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 8.952300
## Warning in dpois(y, mu, log = TRUE): non-integer x = 9.220200
## Warning in dpois(y, mu, log = TRUE): non-integer x = 10.593000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 17.037000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.387700
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.700700
## Warning in dpois(y, mu, log = TRUE): non-integer x = 7.459200
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.923000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.955200
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.059700
## Warning in dpois(y, mu, log = TRUE): non-integer x = 19.633500
## Warning in dpois(y, mu, log = TRUE): non-integer x = 16.620300
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.686400
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.177800
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.815540
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.568808
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.308402
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.637253
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.780542
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.116190
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.893732
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.914210
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.483773
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.119449
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.854612
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.443036
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.542966
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.519023
## Warning in dpois(y, mu, log = TRUE): non-integer x = 6.549697
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.579378
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.902990
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.573131
## Warning in dpois(y, mu, log = TRUE): non-integer x = 6.420451
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.807131
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.853985
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.545844
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.874984
## Warning in dpois(y, mu, log = TRUE): non-integer x = 8.833491
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.801025
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.291941
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.475126
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.325559
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.500902
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.668096
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.159900
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.098700
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.583500
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.715100
## Warning in dpois(y, mu, log = TRUE): non-integer x = 7.426100
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.076000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.647400
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.475700
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.411400
## Warning in dpois(y, mu, log = TRUE): non-integer x = 6.623200
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.315000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.482000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.115000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.121900
## Warning in dpois(y, mu, log = TRUE): non-integer x = 8.937600
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.272600
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.872200
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.192100
## Warning in dpois(y, mu, log = TRUE): non-integer x = 8.710600
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.022000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.704600
## Warning in dpois(y, mu, log = TRUE): non-integer x = 10.655800
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.024900
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.506900
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.364000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.953500
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.067100
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.619100
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.792800
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.629171
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.442955
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.855096
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.477088
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.485309
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.058994
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.773357
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.088490
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.342719
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.601634
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.218156
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.395261
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.484734
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.332778
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.501973
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.541918
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.318244
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.169498
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.058994
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.005624
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.020424
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.102500
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.887451
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.054133
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.769058
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.217672
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.266126
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.229303
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.732580
padj<-m24a$deviance/m24a$df.residual
m24b<-glm((Scirtidae_bio/padj)~maxvol+site*(mu.scalar+I(mu.scalar^2))*(k.scalar+I(k.scalar^2)), family=poisson, data=fulldata);
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.463810
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.394841
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.629457
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.584496
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.335710
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.251783
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.888371
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.241909
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.293747
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.152488
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.067142
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.167855
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.991395
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.311026
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.050829
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.359627
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.363823
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.340956
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.014262
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.862324
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.120081
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.101659
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.167855
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.101659
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.415138
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.209191
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.025415
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.645710
## Warning in dpois(y, mu, log = TRUE): non-integer x = 8.119994
## Warning in dpois(y, mu, log = TRUE): non-integer x = 11.707898
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.832556
## Warning in dpois(y, mu, log = TRUE): non-integer x = 13.218595
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.476275
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.832556
## Warning in dpois(y, mu, log = TRUE): non-integer x = 8.875342
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.720927
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.643719
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.643719
## Warning in dpois(y, mu, log = TRUE): non-integer x = 7.175809
## Warning in dpois(y, mu, log = TRUE): non-integer x = 6.420460
## Warning in dpois(y, mu, log = TRUE): non-integer x = 8.686505
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.476275
## Warning in dpois(y, mu, log = TRUE): non-integer x = 14.918128
## Warning in dpois(y, mu, log = TRUE): non-integer x = 7.931157
## Warning in dpois(y, mu, log = TRUE): non-integer x = 10.008365
## Warning in dpois(y, mu, log = TRUE): non-integer x = 13.973943
## Warning in dpois(y, mu, log = TRUE): non-integer x = 7.742320
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.832556
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.643719
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.776741
## Warning in dpois(y, mu, log = TRUE): non-integer x = 7.931157
## Warning in dpois(y, mu, log = TRUE): non-integer x = 6.231623
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.643719
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.965578
## Warning in dpois(y, mu, log = TRUE): non-integer x = 6.420460
## Warning in dpois(y, mu, log = TRUE): non-integer x = 8.119994
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.909764
## Warning in dpois(y, mu, log = TRUE): non-integer x = 13.596269
## Warning in dpois(y, mu, log = TRUE): non-integer x = 13.409572
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.861054
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.255183
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.577576
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.589737
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.270417
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.538335
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.444320
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.788208
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.040833
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.089355
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.083980
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.172844
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.022870
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.956260
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.269792
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.115497
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.063319
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.068974
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.687639
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.535346
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.367618
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.409681
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.356636
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.932556
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.740018
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.191086
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.587080
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.453778
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.056724
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.239527
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.815480
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.903226
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.953205
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.019334
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.826477
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.100210
## Warning in dpois(y, mu, log = TRUE): non-integer x = 5.647361
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.203729
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.232090
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.865584
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.314684
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.978257
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.153624
## Warning in dpois(y, mu, log = TRUE): non-integer x = 6.698680
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.756725
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.869146
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.445225
## Warning in dpois(y, mu, log = TRUE): non-integer x = 7.149371
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.001969
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.713678
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.130163
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.065878
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.400838
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.025052
## Warning in dpois(y, mu, log = TRUE): non-integer x = 8.238961
## Warning in dpois(y, mu, log = TRUE): non-integer x = 6.974508
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.288039
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.753163
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.761870
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.336883
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.968693
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.526329
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.327545
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.468396
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.794682
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.642551
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.622647
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.889401
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.778265
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.185915
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.647487
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.217802
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.748501
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.243129
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.378929
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.660145
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.694265
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.758341
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.358365
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.229057
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.786814
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.706868
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.336140
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.122510
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.199381
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.136617
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.629836
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.699996
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.486738
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.719970
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.664497
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.558997
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.116273
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.031892
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.271674
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.458536
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.011915
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.779346
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.810738
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.621903
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.048258
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.310068
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.750556
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.792945
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.044560
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.080612
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.655298
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.268146
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.393867
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.471578
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.269363
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.891266
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.411662
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.078676
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.447796
## Warning in dpois(y, mu, log = TRUE): non-integer x = 2.357987
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.171965
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.264024
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.185881
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.358831
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.200204
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.042930
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.024756
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.324530
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.037134
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.143818
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.252468
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.091547
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.165866
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.203413
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.139646
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.630285
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.227409
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.133547
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.071128
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.024756
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.841636
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.008571
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.462651
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.372408
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.442354
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.322726
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.091344
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.111677
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.096224
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.307418
visreg(m24b, "k.scalar", by="site", overlay=FALSE, partial=FALSE, ylab="Scirtid biomass")#driven by site 2 and 5 k (only sig effects)
visreg(m24b, "mu.scalar", by="site", overlay=FALSE, partial=FALSE, ylab="Scirtid biomass")
#single site models sig for CR and CO, this can be seen in the mu plots esp.
tapply(fulldata$Ceratopogonidae_bio, fulldata$site, datacheck)#common in CA, CO, CR, FG, MA
## argentina cardoso colombia costarica frenchguiana
## 0 30 10 11 28
## macae puertorico
## 30 0
allmodel(2,noargprdata$Ceratopogonidae_bio,poisson, noargprdata)#no conting but strong effect of rain and site
## Error in eval(expr, envir, enclos): could not find function "allmodel"
cerato.out<-data.frame(full.d2=numeric(1),noint.d2=numeric(1), intend.d2=numeric(1), site.d2=numeric(1), conting.p=numeric(1),general.p=numeric(1), siterain.p=numeric(1), rainfall.p=numeric(1))
cerato.out[1,]<-allmodel(2,noargprdata$Ceratopogonidae_bio,poisson, noargprdata); row.names(cerato.out)<-"Ceratopogonid biomass"
## Error in eval(expr, envir, enclos): could not find function "allmodel"
kable(cerato.out[,c(1,5,6,7,8)])
|
full.d2 |
conting.p |
general.p |
siterain.p |
rainfall.p |
| Ceratopogonid biomass |
0 |
0 |
0 |
0 |
0 |
tapply(fulldata$Tabanidae_bio, fulldata$site, datacheck)#common in AR only
## argentina cardoso colombia costarica frenchguiana
## 13 5 0 7 1
## macae puertorico
## 5 0
m1<-glm(Tabanidae_bio~maxvol+(mu.scalar+I(mu.scalar^2))*(k.scalar+I(k.scalar^2)), family=poisson, data=ardata);
## Warning in dpois(y, mu, log = TRUE): non-integer x = 16.900000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 46.800000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 14.600000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 4.200000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 12.400000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 7.500000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 6.100000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 13.900000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 14.300000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 9.100000
padj<-m1$deviance/m1$df.residual
m2<-glm((Tabanidae_bio/padj)~maxvol+(mu.scalar+I(mu.scalar^2))*(k.scalar+I(k.scalar^2)), family=poisson, data=ardata)
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.303863
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.610697
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.126414
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.231455
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.324037
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.956680
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.578637
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.470625
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.231455
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.072408
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.103269
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.702080
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.925820
Anova(m2,type=2, test.statistic="LR") #no effect of rain
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.303863
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.610697
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.126414
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.231455
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.324037
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.956680
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.578637
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.470625
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.231455
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.072408
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.103269
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.702080
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.925820
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.303863
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.610697
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.126414
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.231455
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.324037
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.956680
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.578637
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.470625
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.231455
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.072408
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.103269
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.702080
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.925820
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.303863
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.610697
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.126414
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.231455
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.324037
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.956680
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.578637
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.470625
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.231455
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.072408
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.103269
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.702080
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.925820
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.303863
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.610697
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.126414
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.231455
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.324037
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.956680
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.578637
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.470625
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.231455
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.072408
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.103269
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.702080
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.925820
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.303863
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.610697
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.126414
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.231455
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.324037
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.956680
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.578637
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.470625
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.231455
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.072408
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.103269
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.702080
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.925820
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.303863
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.610697
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.126414
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.231455
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.324037
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.956680
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.578637
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.470625
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.231455
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.072408
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.103269
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.702080
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.925820
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.303863
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.610697
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.126414
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.231455
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.324037
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.956680
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.578637
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.470625
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.231455
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.072408
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.103269
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.702080
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.925820
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.303863
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.610697
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.126414
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.231455
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.324037
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.956680
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.578637
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.470625
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.231455
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.072408
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.103269
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.702080
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.925820
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.303863
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.610697
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.126414
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.231455
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.324037
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.956680
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.578637
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.470625
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.231455
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.072408
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.103269
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.702080
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.925820
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.303863
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.610697
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.126414
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.231455
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.324037
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.956680
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.578637
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.470625
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.231455
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.072408
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.103269
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.702080
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.925820
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.303863
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.610697
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.126414
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.231455
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.324037
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.956680
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.578637
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.470625
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.231455
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.072408
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.103269
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.702080
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.925820
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.303863
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.610697
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.126414
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.231455
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.324037
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.956680
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.578637
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.470625
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.231455
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.072408
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.103269
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.702080
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.925820
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.303863
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.610697
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.126414
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.231455
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.324037
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.956680
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.578637
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.470625
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.231455
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.072408
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.103269
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.702080
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.925820
## Analysis of Deviance Table (Type II tests)
##
## Response: (Tabanidae_bio/padj)
## LR Chisq Df Pr(>Chisq)
## maxvol 0.00208 1 0.9636
## mu.scalar 0.63568 1 0.4253
## I(mu.scalar^2) 0.51984 1 0.4709
## k.scalar 1.84944 1 0.1738
## I(k.scalar^2) 1.40126 1 0.2365
## mu.scalar:k.scalar 0.02745 1 0.8684
## mu.scalar:I(k.scalar^2) 0.06885 1 0.7930
## I(mu.scalar^2):k.scalar 0.27462 1 0.6002
## I(mu.scalar^2):I(k.scalar^2) 0.44194 1 0.5062
m3<-glm((Tabanidae_bio/padj)~maxvol, family=poisson, data=ardata)
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.303863
## Warning in dpois(y, mu, log = TRUE): non-integer x = 3.610697
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.126414
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.231455
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.324037
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.956680
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.578637
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.470625
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.231455
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.072408
## Warning in dpois(y, mu, log = TRUE): non-integer x = 1.103269
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.702080
## Warning in dpois(y, mu, log = TRUE): non-integer x = 0.925820
anova(m2,m3,test="LRT") #confirmed - no effect of rain
## Analysis of Deviance Table
##
## Model 1: (Tabanidae_bio/padj) ~ maxvol + (mu.scalar + I(mu.scalar^2)) *
## (k.scalar + I(k.scalar^2))
## Model 2: (Tabanidae_bio/padj) ~ maxvol
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 20 20.000
## 2 28 28.298 -8 -8.2976 0.405
#============hydrology==best aic models
aic.lmx(sqrt(nocadata$prop.driedout.days), gaussian, nocadata)#bestmodel is m5
## Model selection table
## (Int) log(mxv) sit log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m5 1.247 -0.1407 + -0.129900
## m11 1.344 -0.1587 + -0.025110 -0.07153 0.02818
## m9 1.397 -0.1635 + -0.055390 -0.07749
## m10 1.412 -0.1690 + -0.024810 -0.07812 0.05419
## m3 1.208 -0.1351 + -0.024280 0.02848
## m12 1.379 -0.1661 + -0.005476 -0.07272 0.01799 0.02503
## m7 1.171 -0.1367 + -0.066870 0.05856
## m1 1.255 -0.1388 + -0.054850
## m13 1.431 -0.1726 + -0.132100 -0.19130
## m2 1.338 -0.1508 + -0.07547
## m4 1.346 -0.1564 + -0.07609 0.07571
## m0 1.200 -0.1269 +
## m6 1.331 -0.1496 + -0.17320
## m8 1.237 -0.1432 + -0.17310 0.17660
## m14 1.325 -0.1661 + -0.180500 -0.19100 0.21500
## m15 1.368 -0.1707 + -0.069890 -0.21110 0.05798
## m16 1.304 -0.1655 + -0.159400 -0.21030 0.01964 0.10560
## log(mu.scl):sit log(k.scl):sit I(log(mu.scl)^2):sit
## m5 +
## m11
## m9
## m10
## m3
## m12
## m7 + +
## m1
## m13 + +
## m2
## m4
## m0
## m6 +
## m8 +
## m14 + +
## m15 + + +
## m16 + + +
## I(log(k.scl)^2):sit log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m5
## m11 -0.008785
## m9 -0.002522
## m10 -0.002622 -0.09583
## m3
## m12 -0.008527 -0.06211
## m7
## m1
## m13 -0.071390
## m2
## m4
## m0
## m6
## m8 +
## m14 + -0.070960 0.15250
## m15 -0.047880
## m16 + -0.048220 0.28090
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2
## m5
## m11 -0.005930
## m9
## m10
## m3
## m12 -0.005564 0.0315
## m7
## m1
## m13
## m2
## m4
## m0
## m6
## m8
## m14
## m15 0.021840
## m16 0.021200 0.1200
## log(k.scl):log(mu.scl):sit I(log(k.scl)^2):log(mu.scl):sit
## m5
## m11
## m9
## m10
## m3
## m12
## m7
## m1
## m13 +
## m2
## m4
## m0
## m6
## m8
## m14 + +
## m15 +
## m16 + +
## I(log(mu.scl)^2):log(k.scl):sit I(log(k.scl)^2):I(log(mu.scl)^2):sit
## m5
## m11
## m9
## m10
## m3
## m12
## m7
## m1
## m13
## m2
## m4
## m0
## m6
## m8
## m14
## m15 +
## m16 + +
## df logLik AICc delta weight
## m5 14 49.255 -68.0 0.00 0.473
## m11 13 46.906 -65.6 2.35 0.146
## m9 11 44.550 -65.5 2.44 0.140
## m10 13 46.669 -65.1 2.82 0.115
## m3 10 42.313 -63.3 4.64 0.046
## m12 16 49.221 -63.1 4.86 0.042
## m7 20 53.508 -61.7 6.23 0.021
## m1 9 40.056 -61.1 6.91 0.015
## m13 26 58.648 -56.1 11.85 0.001
## m2 9 36.359 -53.7 14.31 0.000
## m4 10 37.031 -52.8 15.20 0.000
## m0 8 32.394 -47.9 20.02 0.000
## m6 14 38.696 -46.8 21.12 0.000
## m8 20 43.409 -41.5 26.43 0.000
## m14 38 69.188 -41.4 26.61 0.000
## m15 38 64.923 -32.8 35.14 0.000
## m16 56 77.856 8.2 76.16 0.000
## Models ranked by AICc(x)
aic.lmx(sqrt(noleakydata$prop.driedout.days), gaussian, noleakydata)
## Model selection table
## (Int) log(mxv) sit log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m5 1.213 -0.1379 + -0.133600
## m9 1.344 -0.1564 + -0.043880 -0.07343
## m11 1.305 -0.1539 + -0.016860 -0.07439 0.02625
## m3 1.158 -0.1288 + -0.016580 0.02648
## m10 1.357 -0.1605 + -0.023960 -0.07365 0.03435
## m1 1.198 -0.1314 + -0.043850
## m7 1.118 -0.1337 + -0.053820 0.07473
## m12 1.329 -0.1593 + -0.001191 -0.07497 0.02121 0.01916
## m2 1.300 -0.1469 + -0.07273
## m4 1.307 -0.1504 + -0.07307 0.04328
## m0 1.155 -0.1221 +
## m13 1.408 -0.1710 + -0.135000 -0.16000
## m6 1.297 -0.1460 + -0.14450
## m8 1.224 -0.1409 + -0.14590 0.13740
## m14 1.328 -0.1666 + -0.180500 -0.16200 0.17300
## m15 1.308 -0.1654 + -0.059140 -0.14760 0.07102
## m16 1.257 -0.1573 + -0.158400 -0.14610 0.01981 0.01434
## log(mu.scl):sit log(k.scl):sit I(log(mu.scl)^2):sit
## m5 +
## m9
## m11
## m3
## m10
## m1
## m7 + +
## m12
## m2
## m4
## m0
## m13 + +
## m6 +
## m8 +
## m14 + +
## m15 + + +
## m16 + + +
## I(log(k.scl)^2):sit log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m5
## m9 -0.003395
## m11 -0.001926
## m3
## m10 -0.003913 -0.06517
## m1
## m7
## m12 -0.002225 -0.04999
## m2
## m4
## m0
## m13 -0.064760
## m6
## m8 +
## m14 + -0.064920 0.14370
## m15 -0.070470
## m16 + -0.071080 0.31210
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2
## m5
## m9
## m11 0.001375
## m3
## m10
## m1
## m7
## m12 0.001648 0.01827
## m2
## m4
## m0
## m13
## m6
## m8
## m14
## m15 -0.006837
## m16 -0.007910 0.16040
## log(k.scl):log(mu.scl):sit I(log(k.scl)^2):log(mu.scl):sit
## m5
## m9
## m11
## m3
## m10
## m1
## m7
## m12
## m2
## m4
## m0
## m13 +
## m6
## m8
## m14 + +
## m15 +
## m16 + +
## I(log(mu.scl)^2):log(k.scl):sit I(log(k.scl)^2):I(log(mu.scl)^2):sit
## m5
## m9
## m11
## m3
## m10
## m1
## m7
## m12
## m2
## m4
## m0
## m13
## m6
## m8
## m14
## m15 +
## m16 + +
## df logLik AICc delta weight
## m5 14 57.469 -84.3 0.00 0.400
## m9 11 53.333 -83.0 1.25 0.214
## m11 13 55.479 -82.7 1.61 0.179
## m3 10 51.083 -80.8 3.47 0.071
## m10 13 54.343 -80.4 3.88 0.057
## m1 9 49.007 -78.9 5.37 0.027
## m7 20 62.043 -78.6 5.72 0.023
## m12 16 56.691 -77.9 6.38 0.016
## m2 9 47.792 -76.5 7.80 0.008
## m4 10 48.036 -74.7 9.57 0.003
## m0 8 43.739 -70.6 13.68 0.000
## m13 26 65.737 -69.9 14.42 0.000
## m6 14 49.534 -68.4 15.87 0.000
## m8 20 55.188 -64.8 19.43 0.000
## m14 38 76.780 -55.4 28.84 0.000
## m15 38 71.026 -43.9 40.35 0.000
## m16 56 83.896 -0.8 83.52 0.000
## Models ranked by AICc(x)
bestmod.propdried<-glm(sqrt(prop.driedout.days)~log(maxvol)+site*log(mu.scalar), family=gaussian, data = nocadata)
par(mfrow=c(1,1));visreg(bestmod.propdried, "mu.scalar", by="site",ylab="Proportion dried out", overlay=TRUE, partial=TRUE, band=FALSE)
par(mfrow=c(2,2)); plot(bestmod.propdried)
#costa rica seems to be very different - this site has very few rows of data so hydro variable
cr.propdried<-glm(sqrt(prop.driedout.days)~log(maxvol)+log(mu.scalar), family=gaussian, data = crdata)
outlierTest(cr.propdried)
##
## No Studentized residuals with Bonferonni p < 0.05
## Largest |rstudent|:
## rstudent unadjusted p-value Bonferonni p
## 208 -1.92165 0.05465 NA
par(mfrow=c(1,1));visreg(cr.propdried, "mu.scalar", ylab="Proportion dried out")#little trend and very variable up if anything
par(mfrow=c(2,2)); plot(cr.propdried)
aic.site(sqrt(crdata$prop.driedout.days), gaussian, crdata)#mo m2 (more affected by k than anything)
## Model selection table
## (Int) log(mxv) log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 3.137 -0.5359
## m2 3.164 -0.5410 -0.09271
## m1 3.048 -0.5169 0.03446
## m4 3.173 -0.5335 -0.09266 -0.15140
## m3 2.983 -0.5095 0.06641 0.02971
## m9 3.713 -0.6460 0.03204 -0.11680
## m10 3.414 -0.5758 0.09312 -0.11410 -0.19560
## m11 3.891 -0.6857 0.06160 -0.16680 0.02830
## m12 3.688 -0.6413 0.16110 -0.16200 0.06810 -0.07813
## log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m2
## m1
## m4
## m3
## m9 -0.09580
## m10 -0.08632 -0.1866
## m11 -0.04430
## m12 -0.04191 -0.3069
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2 df logLik AICc
## m0 3 -1.186 9.3
## m2 4 -0.520 10.6
## m1 4 -0.873 11.3
## m4 5 -0.227 13.0
## m3 5 -0.627 13.8
## m9 6 0.504 14.6
## m10 8 1.318 20.2
## m11 0.05309 8 1.018 20.8
## m12 0.04974 -0.1232 11 2.070 32.5
## delta weight
## m0 0.00 0.452
## m2 1.35 0.231
## m1 2.05 0.162
## m4 3.66 0.073
## m3 4.46 0.049
## m9 5.35 0.031
## m10 10.93 0.002
## m11 11.52 0.001
## m12 23.23 0.000
## Models ranked by AICc(x)
cr.propdriedk<-glm(sqrt(prop.driedout.days)~log(maxvol)+log(k.scalar), family=gaussian, data = crdata)
par(mfrow=c(1,1));visreg(cr.propdriedk, "k.scalar", ylab="Proportion dried out")#negative relationship, consistent with other sites
aic.lmx(sqrt(nocadata$prop.overflow.days), gaussian, nocadata)#m0 and m4, m1, m2
## Model selection table
## (Int) log(mxv) sit log(mu.scl) log(k.scl) log(mu.scl)^2
## m0 0.110200 0.03564 +
## m4 0.078040 0.04504 + 0.01422
## m1 0.100200 0.03779 + 0.009869
## m6 0.134100 0.03150 + 0.12130
## m2 0.085190 0.03997 + 0.01367
## m8 0.060480 0.04145 + 0.12150
## m3 0.095680 0.03815 + 0.012790 0.002722
## m10 0.063700 0.04740 + -0.003468 0.01507
## m9 0.073840 0.04237 + 0.009969 0.01450
## m11 0.072260 0.04220 + 0.012930 0.01157 0.002773
## m5 0.106300 0.03759 + 0.029860
## m12 0.082970 0.04528 + -0.011950 0.01206 -0.007880
## m13 0.078520 0.04241 + 0.030200 0.12630
## m7 0.103000 0.03838 + 0.028440 -0.001382
## m14 0.005645 0.05162 + 0.014880 0.12670
## m15 0.084050 0.04166 + 0.028730 0.12580 -0.001326
## m16 0.051090 0.04655 + -0.004948 0.12660 -0.018020
## log(k.scl)^2 log(mu.scl):sit log(k.scl):sit I(log(mu.scl)^2):sit
## m0
## m4 -0.06889
## m1
## m6 +
## m2
## m8 0.05061 +
## m3
## m10 -0.05890
## m9
## m11
## m5 +
## m12 -0.08899
## m13 + +
## m7 + +
## m14 0.06234 + +
## m15 + + +
## m16 0.01497 + + +
## I(log(k.scl)^2):sit log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m4
## m1
## m6
## m2
## m8 +
## m3
## m10 0.002403 0.04229
## m9 0.002312
## m11 0.005728
## m5
## m12 0.005621 0.07805
## m13 0.019360
## m7
## m14 + 0.019960 0.04985
## m15 0.019840
## m16 + 0.019510 0.10650
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2
## m0
## m4
## m1
## m6
## m2
## m8
## m3
## m10
## m9
## m11 0.0031930
## m5
## m12 0.0030410 0.03338
## m13
## m7
## m14
## m15 0.0004943
## m16 -0.0001072 0.05239
## log(k.scl):log(mu.scl):sit I(log(k.scl)^2):log(mu.scl):sit
## m0
## m4
## m1
## m6
## m2
## m8
## m3
## m10
## m9
## m11
## m5
## m12
## m13 +
## m7
## m14 + +
## m15 +
## m16 + +
## I(log(mu.scl)^2):log(k.scl):sit I(log(k.scl)^2):I(log(mu.scl)^2):sit
## m0
## m4
## m1
## m6
## m2
## m8
## m3
## m10
## m9
## m11
## m5
## m12
## m13
## m7
## m14
## m15 +
## m16 + +
## df logLik AICc delta weight
## m0 8 130.568 -244.3 0.00 0.232
## m4 10 132.547 -243.8 0.50 0.180
## m1 9 131.279 -243.5 0.80 0.156
## m6 14 136.962 -243.4 0.92 0.147
## m2 9 130.948 -242.8 1.46 0.112
## m8 20 143.506 -241.7 2.56 0.064
## m3 10 131.335 -241.4 2.93 0.054
## m10 13 134.012 -239.8 4.46 0.025
## m9 11 131.688 -239.8 4.49 0.025
## m11 13 131.771 -235.4 8.94 0.003
## m5 14 132.523 -234.5 9.79 0.002
## m12 16 134.542 -233.7 10.55 0.001
## m13 26 147.361 -233.5 10.75 0.001
## m7 20 134.284 -223.3 21.01 0.000
## m14 38 157.324 -217.6 26.67 0.000
## m15 38 150.230 -203.4 40.86 0.000
## m16 56 167.004 -170.1 74.19 0.000
## Models ranked by AICc(x)
bestmod.propoverflow<-glm(sqrt(prop.overflow.days)~log(maxvol)+site+log(k.scalar)+I(log(k.scalar)^2), family=gaussian, data = nocadata)
par(mfrow=c(1,1));visreg(bestmod.propoverflow, "k.scalar", by="site",ylab="Proportion overflow", overlay=TRUE, partial=FALSE, band=FALSE)
par(mfrow=c(2,2)); plot(bestmod.propoverflow)
aic.lmx(log(nocadata$cv.depth), gaussian, nocadata)
## Model selection table
## (Int) log(mxv) sit log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m5 6.851 -0.4487 + -0.33240
## m9 7.272 -0.5142 + -0.15430 -0.2018
## m10 7.316 -0.5355 + -0.09584 -0.2043 0.2489
## m11 7.213 -0.5124 + -0.09672 -0.2311 0.05372
## m13 7.474 -0.5570 + -0.34010 -0.5747
## m12 7.317 -0.5395 + -0.07063 -0.2354 0.02363 0.1649
## m3 6.808 -0.4421 + -0.09424 0.05461
## m1 6.899 -0.4493 + -0.15290
## m7 6.714 -0.4425 + -0.21210 0.11200
## m4 7.148 -0.5021 + -0.2070 0.2888
## m2 7.118 -0.4809 + -0.2047
## m6 7.206 -0.4961 + -0.5308
## m14 7.274 -0.5475 + -0.45950 -0.5743 0.4550
## m0 6.744 -0.4159 +
## m15 7.349 -0.5527 + -0.22190 -0.6593 0.11010
## m8 6.991 -0.4789 + -0.5303 0.3599
## m16 7.233 -0.5504 + -0.39260 -0.6589 0.06280 0.3205
## log(mu.scl):sit log(k.scl):sit I(log(mu.scl)^2):sit
## m5 +
## m9
## m10
## m11
## m13 + +
## m12
## m3
## m1
## m7 + +
## m4
## m2
## m6 +
## m14 + +
## m0
## m15 + + +
## m8 +
## m16 + + +
## I(log(k.scl)^2):sit log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m5
## m9 0.02659
## m10 0.02621 -0.18390
## m11 0.06126
## m13 -0.17260
## m12 0.06220 -0.08444
## m3
## m1
## m7
## m4
## m2
## m6
## m14 + -0.17200 0.37500
## m0
## m15 -0.07273
## m8 +
## m16 + -0.07288 0.53350
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2
## m5
## m9
## m10
## m11 0.03234
## m13
## m12 0.03368 0.09289
## m3
## m1
## m7
## m4
## m2
## m6
## m14
## m0
## m15 0.09305
## m8
## m16 0.09277 0.14780
## log(k.scl):log(mu.scl):sit I(log(k.scl)^2):log(mu.scl):sit
## m5
## m9
## m10
## m11
## m13 +
## m12
## m3
## m1
## m7
## m4
## m2
## m6
## m14 + +
## m0
## m15 +
## m8
## m16 + +
## I(log(mu.scl)^2):log(k.scl):sit I(log(k.scl)^2):I(log(mu.scl)^2):sit
## m5
## m9
## m10
## m11
## m13
## m12
## m3
## m1
## m7
## m4
## m2
## m6
## m14
## m0
## m15 +
## m8
## m16 + +
## df logLik AICc delta weight
## m5 14 -125.606 281.8 0.00 0.666
## m9 11 -130.867 285.3 3.55 0.113
## m10 13 -128.570 285.3 3.58 0.111
## m11 13 -129.530 287.3 5.49 0.043
## m13 26 -113.484 288.1 6.39 0.027
## m12 16 -127.012 289.4 7.60 0.015
## m3 10 -134.482 290.3 8.51 0.009
## m1 9 -135.653 290.4 8.61 0.009
## m7 20 -123.025 291.3 9.58 0.006
## m4 10 -138.574 298.4 16.69 0.000
## m2 9 -139.958 299.0 17.22 0.000
## m6 14 -136.592 303.7 21.97 0.000
## m14 38 -103.773 304.6 22.81 0.000
## m0 8 -144.065 305.0 23.22 0.000
## m15 38 -104.742 306.5 24.75 0.000
## m8 20 -131.489 308.3 26.50 0.000
## m16 56 -91.556 347.0 65.26 0.000
## Models ranked by AICc(x)
aic.lmx(log(noleakydata$cv.depth), gaussian, noleakydata)#same best model m5 m9 m11, even new leaky
## Model selection table
## (Int) log(mxv) sit log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m5 6.725 -0.4401 + -0.34780
## m9 7.104 -0.4951 + -0.12480 -0.1906
## m11 7.068 -0.4983 + -0.06472 -0.2335 0.05849
## m10 7.150 -0.5144 + -0.09354 -0.1923 0.20640
## m3 6.632 -0.4239 + -0.06423 0.05912
## m12 7.140 -0.5206 + -0.04356 -0.2367 0.04674 0.17200
## m1 6.722 -0.4297 + -0.12510
## m7 6.508 -0.4309 + -0.16190 0.17420
## m2 6.992 -0.4703 + -0.1974
## m4 7.027 -0.4879 + -0.1991 0.21470
## m13 7.364 -0.5491 + -0.35290 -0.4354
## m0 6.600 -0.4031 +
## m6 7.080 -0.4845 + -0.4017
## m8 6.927 -0.4678 + -0.4030 0.17610
## m14 7.236 -0.5409 + -0.45880 -0.4382 0.25860
## m15 7.094 -0.5291 + -0.17740 -0.3971 0.16390
## m16 7.012 -0.5120 + -0.38770 -0.3919 0.06360 -0.05858
## log(mu.scl):sit log(k.scl):sit I(log(mu.scl)^2):sit
## m5 +
## m9
## m11
## m10
## m3
## m12
## m1
## m7 + +
## m2
## m4
## m13 + +
## m0
## m6 +
## m8 +
## m14 + +
## m15 + + +
## m16 + + +
## I(log(k.scl)^2):sit log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m5
## m9 0.03309
## m11 0.08007
## m10 0.03155 -0.10530
## m3
## m12 0.07965 -0.06892
## m1
## m7
## m2
## m4
## m13 -0.14310
## m0
## m6
## m8 +
## m14 + -0.14330 0.33340
## m15 -0.16600
## m16 + -0.16800 0.66250
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2
## m5
## m9
## m11 0.04671
## m10
## m3
## m12 0.04796 0.04307
## m1
## m7
## m2
## m4
## m13
## m0
## m6
## m8
## m14
## m15 -0.02533
## m16 -0.02858 0.31560
## log(k.scl):log(mu.scl):sit I(log(k.scl)^2):log(mu.scl):sit
## m5
## m9
## m11
## m10
## m3
## m12
## m1
## m7
## m2
## m4
## m13 +
## m0
## m6
## m8
## m14 + +
## m15 +
## m16 + +
## I(log(mu.scl)^2):log(k.scl):sit I(log(k.scl)^2):I(log(mu.scl)^2):sit
## m5
## m9
## m11
## m10
## m3
## m12
## m1
## m7
## m2
## m4
## m13
## m0
## m6
## m8
## m14
## m15 +
## m16 + +
## df logLik AICc delta weight
## m5 14 -106.538 243.7 0.00 0.309
## m9 11 -110.121 243.9 0.15 0.287
## m11 13 -108.192 244.7 0.94 0.193
## m10 13 -108.733 245.8 2.02 0.113
## m3 10 -113.509 248.4 4.64 0.030
## m12 16 -106.607 248.7 4.97 0.026
## m1 9 -115.057 249.2 5.48 0.020
## m7 20 -102.118 249.8 6.03 0.015
## m2 9 -117.020 253.1 9.41 0.003
## m4 10 -116.122 253.6 9.87 0.002
## m13 26 -96.434 254.5 10.75 0.001
## m0 8 -121.451 259.8 16.04 0.000
## m6 14 -115.187 261.0 17.30 0.000
## m8 20 -109.012 263.6 19.82 0.000
## m14 38 -85.652 269.4 25.69 0.000
## m15 38 -85.749 269.6 25.88 0.000
## m16 56 -72.266 311.6 67.83 0.000
## Models ranked by AICc(x)
bestmod.cvdepth<-glm(log(cv.depth)~log(maxvol)+site*log(mu.scalar), family=gaussian, data = nocadata)
bestmod.cvdepth<-glm(log(cv.depth)~log(maxvol)+site*log(mu.scalar), family=gaussian, data = noleakydata)
par(mfrow=c(2,2)); plot(bestmod.cvdepth)
par(mfrow=c(1,1));visreg(bestmod.cvdepth, "mu.scalar", by="site",ylab="cv depth",trans=exp, overlay=TRUE, partial=TRUE, band=FALSE)
#ar ma and to a lesser degree pr have strongest decline in cv depth with mu; cr fg and co have no or variable change in cv with mu
aic.lmx((nocadata$long_dry)^0.1, gaussian, nocadata)#m9
## Model selection table
## (Int) log(mxv) sit log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m9 3.101 -0.3579 + -0.13720 -0.1837
## m11 3.050 -0.3575 + -0.07974 -0.2208 0.05368
## m10 3.108 -0.3587 + -0.11160 -0.1838 -0.006641
## m3 2.672 -0.2920 + -0.07743 0.05451
## m1 2.763 -0.2991 + -0.13590
## m12 3.097 -0.3621 + -0.07608 -0.2215 0.03321 -0.064170
## m5 2.638 -0.2762 + -0.10360
## m2 2.959 -0.3274 + -0.1824
## m4 2.960 -0.3280 + -0.1825 0.008266
## m13 2.960 -0.3322 + -0.10760 -0.2329
## m7 2.548 -0.2653 + -0.07066 0.03009
## m6 2.922 -0.3211 + -0.2266
## m0 2.625 -0.2695 +
## m8 2.787 -0.3010 + -0.2261 0.062200
## m14 2.737 -0.3017 + -0.21870 -0.2316 0.148800
## m15 2.971 -0.3386 + -0.07716 -0.3964 0.02884
## m16 2.717 -0.2752 + -0.37000 -0.3866 -0.14380 -0.347400
## log(mu.scl):sit log(k.scl):sit I(log(mu.scl)^2):sit
## m9
## m11
## m10
## m3
## m1
## m12
## m5 +
## m2
## m4
## m13 + +
## m7 + +
## m6 +
## m0
## m8 +
## m14 + +
## m15 + + +
## m16 + + +
## I(log(k.scl)^2):sit log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m9 0.008381
## m11 0.051980
## m10 0.008367 -0.08018
## m3
## m1
## m12 0.052140 -0.01193
## m5
## m2
## m4
## m13 -0.024410
## m7
## m6
## m0
## m8 +
## m14 + -0.022410 0.35370
## m15 0.167000
## m16 + 0.162800 0.93170
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2
## m9
## m11 0.04071
## m10
## m3
## m1
## m12 0.04093 0.06373
## m5
## m2
## m4
## m13
## m7
## m6
## m0
## m8
## m14
## m15 0.17910
## m16 0.17130 0.54240
## log(k.scl):log(mu.scl):sit I(log(k.scl)^2):log(mu.scl):sit
## m9
## m11
## m10
## m3
## m1
## m12
## m5
## m2
## m4
## m13 +
## m7
## m6
## m0
## m8
## m14 + +
## m15 +
## m16 + +
## I(log(mu.scl)^2):log(k.scl):sit I(log(k.scl)^2):I(log(mu.scl)^2):sit
## m9
## m11
## m10
## m3
## m1
## m12
## m5
## m2
## m4
## m13
## m7
## m6
## m0
## m8
## m14
## m15 +
## m16 + +
## df logLik AICc delta weight
## m9 11 -107.763 239.1 0.00 0.509
## m11 13 -105.926 240.0 0.95 0.317
## m10 13 -107.589 243.4 4.27 0.060
## m3 10 -111.087 243.5 4.38 0.057
## m1 9 -112.598 244.3 5.16 0.039
## m12 16 -105.635 246.6 7.51 0.012
## m5 14 -108.754 248.1 8.96 0.006
## m2 9 -116.974 253.0 13.91 0.000
## m4 10 -116.973 255.2 16.15 0.000
## m13 26 -97.459 256.1 17.00 0.000
## m7 20 -105.999 257.3 18.18 0.000
## m6 14 -113.573 257.7 18.59 0.000
## m0 8 -121.185 259.2 20.11 0.000
## m8 20 -110.410 266.1 27.00 0.000
## m14 38 -91.489 280.0 40.90 0.000
## m15 38 -92.035 281.1 41.99 0.000
## m16 56 -81.881 327.7 88.57 0.000
## Models ranked by AICc(x)
aic.lmx((noleakydata$long_dry)^0.1, gaussian, noleakydata)
## Model selection table
## (Int) log(mxv) sit log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m9 3.085 -0.3553 + -0.12670 -0.1822
## m11 3.086 -0.3637 + -0.07342 -0.2465 0.05194
## m3 2.643 -0.2881 + -0.07252 0.05259
## m1 2.723 -0.2933 + -0.12670
## m10 3.085 -0.3532 + -0.11130 -0.1818 -0.03710
## m12 3.132 -0.3662 + -0.08123 -0.2467 0.02828 -0.10170
## m5 2.606 -0.2721 + -0.10520
## m2 2.961 -0.3281 + -0.1816
## m4 2.954 -0.3246 + -0.1812 -0.04268
## m6 2.930 -0.3227 + -0.2235
## m0 2.599 -0.2664 +
## m7 2.517 -0.2628 + -0.06442 0.03771
## m13 2.958 -0.3322 + -0.10810 -0.2280
## m8 2.807 -0.3044 + -0.2233 0.05816
## m14 2.754 -0.3047 + -0.21900 -0.2280 0.14360
## m15 2.993 -0.3421 + -0.07825 -0.4017 0.02780
## m16 2.742 -0.2797 + -0.37050 -0.3793 -0.14390 -0.35860
## log(mu.scl):sit log(k.scl):sit I(log(mu.scl)^2):sit
## m9
## m11
## m3
## m1
## m10
## m12
## m5 +
## m2
## m4
## m6 +
## m0
## m7 + +
## m13 + +
## m8 +
## m14 + +
## m15 + + +
## m16 + + +
## I(log(k.scl)^2):sit log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m9 -0.003147
## m11 0.066580
## m3
## m1
## m10 -0.003246 -0.04921
## m12 0.066420 0.02281
## m5
## m2
## m4
## m6
## m0
## m7
## m13 -0.023380
## m8 +
## m14 + -0.021820 0.35290
## m15 0.168900
## m16 + 0.160200 0.93630
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2
## m9
## m11 0.06946
## m3
## m1
## m10
## m12 0.06958 0.07595
## m5
## m2
## m4
## m6
## m0
## m7
## m13
## m8
## m14
## m15 0.18170
## m16 0.16830 0.54760
## log(k.scl):log(mu.scl):sit I(log(k.scl)^2):log(mu.scl):sit
## m9
## m11
## m3
## m1
## m10
## m12
## m5
## m2
## m4
## m6
## m0
## m7
## m13 +
## m8
## m14 + +
## m15 +
## m16 + +
## I(log(mu.scl)^2):log(k.scl):sit I(log(k.scl)^2):I(log(mu.scl)^2):sit
## m9
## m11
## m3
## m1
## m10
## m12
## m5
## m2
## m4
## m6
## m0
## m7
## m13
## m8
## m14
## m15 +
## m16 + +
## df logLik AICc delta weight
## m9 11 -102.439 228.5 0.00 0.443
## m11 13 -100.275 228.8 0.32 0.377
## m3 10 -105.536 232.4 3.91 0.063
## m1 9 -106.880 232.9 4.35 0.050
## m10 13 -102.361 233.0 4.49 0.047
## m12 16 -100.027 235.5 7.02 0.013
## m5 14 -103.692 238.0 9.53 0.004
## m2 9 -109.974 239.1 10.54 0.002
## m4 10 -109.936 241.2 12.71 0.001
## m6 14 -106.471 243.6 15.08 0.000
## m0 8 -114.051 245.0 16.46 0.000
## m7 20 -100.603 246.7 18.21 0.000
## m13 26 -93.429 248.5 19.96 0.000
## m8 20 -102.049 249.6 21.11 0.000
## m14 38 -86.392 270.9 42.39 0.000
## m15 38 -88.064 274.2 45.73 0.000
## m16 56 -77.576 322.2 93.67 0.000
## Models ranked by AICc(x)
#check this!
bestmod.longdry<-glm((long_dry)^0.1~log(maxvol)+site+log(mu.scalar)*log(k.scalar), family=gaussian, data = nocadata)
par(mfrow=c(1,1));visreg(bestmod.longdry, "mu.scalar", by="site",ylab="Longest dry period", overlay=TRUE, partial=FALSE, band=FALSE)
par(mfrow=c(1,1));visreg(bestmod.longdry, "k.scalar", by="site",ylab="Longest dry period", overlay=TRUE, partial=FALSE, band=FALSE)
visreg2d(bestmod.longdry, "mu.scalar", "k.scalar", zlab="Longest dry period",plot.type="persp")
par(mfrow=c(2,2)); plot(bestmod.longdry)
aic.lmx(fulldata$mean_temp, gaussian, fulldata)#m0 and m4, m1, m2
## Model selection table
## (Int) log(mxv) sit log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 19.03 0.49310 +
## m1 19.49 0.39970 + -0.308600
## m2 18.86 0.52270 + 0.09967
## m3 18.91 0.45680 + -0.011710 0.2745
## m4 18.86 0.52740 + 0.10040 -0.08328
## m9 19.43 0.41030 + -0.305200 -0.10140
## m11 18.55 0.51940 + -0.005374 0.14350 0.2758
## m10 19.53 0.40950 + -0.007928 -0.09734 -0.29600
## m5 19.89 0.26430 + -1.892000
## m6 17.90 0.68950 + -1.66300
## m7 20.97 0.28280 + -3.285000 -1.3020
## m12 20.20 0.35870 + -0.437700 0.12300 -0.3942 -2.20000
## m8 17.26 0.66870 + -1.66300 2.36800
## m13 19.18 0.38690 + -1.883000 -2.11800
## m15 19.67 0.50800 + -3.265000 -1.10600 -1.2980
## m14 20.17 0.04799 + -2.688000 -2.13300 2.99500
## m16 19.74 0.41300 + -4.597000 -1.12000 -1.8180 1.49400
## log(mu.scl):sit log(k.scl):sit I(log(mu.scl)^2):sit
## m0
## m1
## m2
## m3
## m4
## m9
## m11
## m10
## m5 +
## m6 +
## m7 + +
## m12
## m8 +
## m13 + +
## m15 + + +
## m14 + +
## m16 + + +
## I(log(k.scl)^2):sit log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m1
## m2
## m3
## m4
## m9 -0.7573
## m11 -1.0310
## m10 -0.7548 -0.9295
## m5
## m6
## m7
## m12 -1.0220 1.2760
## m8 +
## m13 -1.8280
## m15 -3.0040
## m14 + -1.8500 2.4400
## m16 + -2.9970 4.1340
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2
## m0
## m1
## m2
## m3
## m4
## m9
## m11 -0.2564
## m10
## m5
## m6
## m7
## m12 -0.2485 2.046
## m8
## m13
## m15 -1.1060
## m14
## m16 -1.0940 1.618
## log(k.scl):log(mu.scl):sit I(log(k.scl)^2):log(mu.scl):sit
## m0
## m1
## m2
## m3
## m4
## m9
## m11
## m10
## m5
## m6
## m7
## m12
## m8
## m13 +
## m15 +
## m14 + +
## m16 + +
## I(log(mu.scl)^2):log(k.scl):sit I(log(k.scl)^2):I(log(mu.scl)^2):sit
## m0
## m1
## m2
## m3
## m4
## m9
## m11
## m10
## m5
## m6
## m7
## m12
## m8
## m13
## m15 +
## m14
## m16 + +
## df logLik AICc delta weight
## m0 8 -547.657 1112.2 0.00 0.456
## m1 9 -547.350 1113.8 1.61 0.204
## m2 9 -547.648 1114.4 2.21 0.151
## m3 10 -547.101 1115.5 3.36 0.085
## m4 10 -547.647 1116.6 4.45 0.049
## m9 11 -546.742 1117.1 4.92 0.039
## m11 13 -546.420 1121.1 8.92 0.005
## m10 13 -546.596 1121.5 9.27 0.004
## m5 14 -545.693 1122.0 9.83 0.003
## m6 14 -546.693 1124.0 11.83 0.001
## m7 20 -540.492 1126.4 14.26 0.000
## m12 16 -545.569 1126.6 14.40 0.000
## m8 20 -546.388 1138.2 26.05 0.000
## m13 26 -543.004 1147.5 35.31 0.000
## m15 38 -537.365 1172.5 60.34 0.000
## m14 38 -540.742 1179.3 67.10 0.000
## m16 56 -531.110 1228.3 116.14 0.000
## Models ranked by AICc(x)
#bestmodel is m7
bestmod.meantemp<-glm(mean_temp~log(maxvol)+site*(log(mu.scalar)+I(log(mu.scalar)^2)),family=gaussian, data = fulldata)
visreg(bestmod.meantemp, "mu.scalar", by="site",ylab="Mean temp", overlay=FALSE, partial=FALSE, band=FALSE)
aic.lmx(fulldata$change_mean_temp, gaussian, fulldata) #m7mu2 x site
## Model selection table
## (Int) log(mxv) sit log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 -2.8440 0.49310 +
## m1 -2.3810 0.39970 + -0.308600
## m2 -3.0150 0.52270 + 0.09967
## m3 -2.9590 0.45680 + -0.011710 0.2745
## m4 -3.0150 0.52740 + 0.10040 -0.08328
## m9 -2.4410 0.41030 + -0.305200 -0.10140
## m11 -3.3200 0.51940 + -0.005374 0.14350 0.2758
## m10 -2.3420 0.40950 + -0.007928 -0.09734 -0.29600
## m5 -1.9880 0.26430 + -1.892000
## m6 -3.9760 0.68950 + -1.66300
## m7 -0.9076 0.28280 + -3.285000 -1.3020
## m12 -1.6770 0.35870 + -0.437700 0.12300 -0.3942 -2.20000
## m8 -4.6150 0.66870 + -1.66300 2.36800
## m13 -2.6920 0.38690 + -1.883000 -2.11800
## m15 -2.2060 0.50800 + -3.265000 -1.10600 -1.2980
## m14 -1.7030 0.04799 + -2.688000 -2.13300 2.99500
## m16 -2.1370 0.41300 + -4.597000 -1.12000 -1.8180 1.49400
## log(mu.scl):sit log(k.scl):sit I(log(mu.scl)^2):sit
## m0
## m1
## m2
## m3
## m4
## m9
## m11
## m10
## m5 +
## m6 +
## m7 + +
## m12
## m8 +
## m13 + +
## m15 + + +
## m14 + +
## m16 + + +
## I(log(k.scl)^2):sit log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m1
## m2
## m3
## m4
## m9 -0.7573
## m11 -1.0310
## m10 -0.7548 -0.9295
## m5
## m6
## m7
## m12 -1.0220 1.2760
## m8 +
## m13 -1.8280
## m15 -3.0040
## m14 + -1.8500 2.4400
## m16 + -2.9970 4.1340
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2
## m0
## m1
## m2
## m3
## m4
## m9
## m11 -0.2564
## m10
## m5
## m6
## m7
## m12 -0.2485 2.046
## m8
## m13
## m15 -1.1060
## m14
## m16 -1.0940 1.618
## log(k.scl):log(mu.scl):sit I(log(k.scl)^2):log(mu.scl):sit
## m0
## m1
## m2
## m3
## m4
## m9
## m11
## m10
## m5
## m6
## m7
## m12
## m8
## m13 +
## m15 +
## m14 + +
## m16 + +
## I(log(mu.scl)^2):log(k.scl):sit I(log(k.scl)^2):I(log(mu.scl)^2):sit
## m0
## m1
## m2
## m3
## m4
## m9
## m11
## m10
## m5
## m6
## m7
## m12
## m8
## m13
## m15 +
## m14
## m16 + +
## df logLik AICc delta weight
## m0 8 -547.657 1112.2 0.00 0.456
## m1 9 -547.350 1113.8 1.61 0.204
## m2 9 -547.648 1114.4 2.21 0.151
## m3 10 -547.101 1115.5 3.36 0.085
## m4 10 -547.647 1116.6 4.45 0.049
## m9 11 -546.742 1117.1 4.92 0.039
## m11 13 -546.420 1121.1 8.92 0.005
## m10 13 -546.596 1121.5 9.27 0.004
## m5 14 -545.693 1122.0 9.83 0.003
## m6 14 -546.693 1124.0 11.83 0.001
## m7 20 -540.492 1126.4 14.26 0.000
## m12 16 -545.569 1126.6 14.40 0.000
## m8 20 -546.388 1138.2 26.05 0.000
## m13 26 -543.004 1147.5 35.31 0.000
## m15 38 -537.365 1172.5 60.34 0.000
## m14 38 -540.742 1179.3 67.10 0.000
## m16 56 -531.110 1228.3 116.14 0.000
## Models ranked by AICc(x)
bestmod.meantemp<-glm(change_mean_temp~log(maxvol)+site*(log(mu.scalar)+I(log(mu.scalar)^2)),family=gaussian, data = fulldata)
visreg(bestmod.meantemp, "mu.scalar", by="site",ylab="Mean temp", overlay=FALSE, partial=TRUE, band=FALSE)
aic.lmx(fulldata$change_cv_temp, gaussian, fulldata) #m2 site+k
## Model selection table
## (Int) log(mxv) sit log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m5 -1.4780 0.2236 + -0.7702
## m1 -1.7380 0.2952 + -0.1439
## m9 -1.3640 0.2305 + -0.1434 -0.2582
## m0 -1.9540 0.3388 +
## m3 -1.5710 0.2787 + -0.2296 -0.07922
## m2 -1.6150 0.2800 + -0.1980
## m7 -0.8905 0.2071 + -1.3500 -0.54060
## m11 -1.3390 0.2387 + -0.2292 -0.1383 -0.08004
## m4 -1.6150 0.2766 + -0.1985 0.06165
## m10 -1.3750 0.2276 + -0.1764 -0.2592 0.08724
## m6 -2.1340 0.3700 + -0.7361
## m12 -1.0420 0.2048 + -0.4095 -0.1441 -0.21390 -0.30720
## m13 -1.5120 0.2295 + -0.7698 -0.9504
## m8 -2.4960 0.3947 + -0.7354 0.68660
## m15 -1.2160 0.2636 + -1.3450 -0.2036 -0.53970
## m14 -1.6020 0.1886 + -1.1900 -0.9521 1.01600
## m16 -1.1600 0.2544 + -2.1460 -0.2050 -0.89890 -0.01043
## log(mu.scl):sit log(k.scl):sit I(log(mu.scl)^2):sit
## m5 +
## m1
## m9
## m0
## m3
## m2
## m7 + +
## m11
## m4
## m10
## m6 +
## m12
## m13 + +
## m8 +
## m15 + + +
## m14 + +
## m16 + + +
## I(log(k.scl)^2):sit log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m5
## m1
## m9 -0.2251
## m0
## m3
## m2
## m7
## m11 -0.3654
## m4
## m10 -0.2256 0.1034
## m6
## m12 -0.3645 0.5480
## m13 -0.8598
## m8 +
## m15 -1.7340
## m14 + -0.8625 1.3040
## m16 + -1.7330 2.4970
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2
## m5
## m1
## m9
## m0
## m3
## m2
## m7
## m11 -0.1309
## m4
## m10
## m6
## m12 -0.1293 0.4082
## m13
## m8
## m15 -0.8180
## m14
## m16 -0.8169 1.1210
## log(k.scl):log(mu.scl):sit I(log(k.scl)^2):log(mu.scl):sit
## m5
## m1
## m9
## m0
## m3
## m2
## m7
## m11
## m4
## m10
## m6
## m12
## m13 +
## m8
## m15 +
## m14 + +
## m16 + +
## I(log(mu.scl)^2):log(k.scl):sit I(log(k.scl)^2):I(log(mu.scl)^2):sit
## m5
## m1
## m9
## m0
## m3
## m2
## m7
## m11
## m4
## m10
## m6
## m12
## m13
## m8
## m15 +
## m14
## m16 + +
## df logLik AICc delta weight
## m5 14 -271.019 572.7 0.00 0.275
## m1 9 -277.230 573.6 0.89 0.176
## m9 11 -275.267 574.2 1.49 0.130
## m0 8 -278.681 574.2 1.57 0.126
## m3 10 -276.774 574.9 2.23 0.090
## m2 9 -277.916 574.9 2.26 0.089
## m7 20 -265.642 576.7 4.08 0.036
## m11 13 -274.369 577.0 4.34 0.031
## m4 10 -277.904 577.2 4.49 0.029
## m10 13 -275.215 578.7 6.03 0.014
## m6 14 -276.041 582.7 10.04 0.002
## m12 16 -273.693 582.8 10.17 0.002
## m13 26 -264.396 590.3 17.61 0.000
## m8 20 -275.589 596.6 23.97 0.000
## m15 38 -254.980 607.8 35.09 0.000
## m14 38 -262.352 622.5 49.84 0.000
## m16 56 -250.759 667.6 94.96 0.000
## Models ranked by AICc(x)
bestmod.meantemp<-glm(change_cv_temp~log(maxvol)+site+log(k.scalar),family=gaussian, data = fulldata)
visreg(bestmod.meantemp, "k.scalar", by="site",ylab="cv temp", overlay=FALSE, partial=TRUE, band=FALSE)
aic.lmx(fulldata$ph.final,gaussian, fulldata)#no effect
## Model selection table
## (Int) log(mxv) sit log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m0 7.154 -0.17730 +
## m1 7.140 -0.17370 + 0.02789
## m7 6.988 -0.15300 + 0.07192 0.03670
## m3 7.034 -0.16220 + 0.07468 0.04334
## m2 7.197 -0.18470 + -0.020090
## m4 7.078 -0.17640 + -0.018540 0.2204
## m5 7.185 -0.18150 + 0.03062
## m9 7.159 -0.17690 + 0.02860 -0.003047
## m11 7.046 -0.16380 + 0.07409 0.036250 0.04148
## m10 7.037 -0.16820 + 0.02046 -0.001746 0.2217
## m8 7.061 -0.17230 + -0.265700 0.1997
## m6 7.350 -0.21120 + -0.266800
## m12 6.948 -0.15690 + 0.05064 0.037320 0.02679 0.1793
## m13 7.515 -0.23880 + 0.02658 -0.303700
## m14 7.112 -0.18340 + -0.05356 -0.301300 0.2645
## m15 7.364 -0.21820 + 0.06615 -0.312600 0.03560
## m16 6.682 -0.08195 + -0.22030 -0.291500 -0.16560 -0.3220
## log(mu.scl):sit log(k.scl):sit I(log(mu.scl)^2):sit
## m0
## m1
## m7 + +
## m3
## m2
## m4
## m5 +
## m9
## m11
## m10
## m8 +
## m6 +
## m12
## m13 + +
## m14 + +
## m15 + + +
## m16 + + +
## I(log(k.scl)^2):sit log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m0
## m1
## m7
## m3
## m2
## m4
## m5
## m9 0.07133
## m11 0.02604
## m10 0.07054 0.02209
## m8 +
## m6
## m12 0.02479 0.06774
## m13 -0.14790
## m14 + -0.14430 0.26240
## m15 -0.13510
## m16 + -0.14420 0.93190
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2
## m0
## m1
## m7
## m3
## m2
## m4
## m5
## m9
## m11 -0.041020
## m10
## m8
## m6
## m12 -0.041090 0.04502
## m13
## m14
## m15 0.010710
## m16 -0.006067 0.63540
## log(k.scl):log(mu.scl):sit I(log(k.scl)^2):log(mu.scl):sit
## m0
## m1
## m7
## m3
## m2
## m4
## m5
## m9
## m11
## m10
## m8
## m6
## m12
## m13 +
## m14 + +
## m15 +
## m16 + +
## I(log(mu.scl)^2):log(k.scl):sit I(log(k.scl)^2):I(log(mu.scl)^2):sit
## m0
## m1
## m7
## m3
## m2
## m4
## m5
## m9
## m11
## m10
## m8
## m6
## m12
## m13
## m14
## m15 +
## m16 + +
## df logLik AICc delta weight
## m0 9 -125.212 269.4 0.00 0.292
## m1 10 -124.819 270.9 1.44 0.142
## m7 23 -109.125 270.9 1.44 0.142
## m3 11 -123.861 271.2 1.78 0.120
## m2 10 -125.156 271.5 2.12 0.101
## m4 11 -124.088 271.7 2.23 0.096
## m5 16 -119.161 273.4 4.03 0.039
## m9 12 -123.917 273.6 4.17 0.036
## m11 14 -122.723 275.8 6.41 0.012
## m10 14 -122.871 276.1 6.71 0.010
## m8 23 -112.055 276.7 7.30 0.008
## m6 16 -122.346 279.8 10.40 0.002
## m12 17 -121.612 280.8 11.34 0.001
## m13 30 -107.349 286.3 16.90 0.000
## m14 44 -91.734 298.6 29.17 0.000
## m15 44 -98.670 312.5 43.05 0.000
## m16 65 -72.593 343.8 74.41 0.000
## Models ranked by AICc(x)
aic.lmx(fulldata$turbidity.final,gaussian, fulldata)#m3 site +(mu+mu2)
## Model selection table
## (Int) log(mxv) sit log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m3 -113.50 22.11 + -10.980 41.110
## m7 -157.50 34.32 + 16.260 24.330
## m1 -51.20 17.81 + -54.990
## m6 -179.60 42.41 + -62.120
## m5 -71.63 23.24 + -10.580
## m9 -186.40 41.25 + -55.100 67.160
## m11 -271.80 49.36 + -9.827 82.070 42.350
## m0 -129.00 33.63 +
## m2 -243.80 53.53 + 58.140
## m13 -295.50 62.18 + -7.839 -59.660
## m4 -240.30 56.45 + 59.090 -63.46
## m10 -183.40 44.11 + -51.330 67.960 -60.89
## m12 -290.90 54.23 + 9.892 82.840 55.130 -28.16
## m8 -222.80 45.26 + -62.050 83.38
## m15 -451.90 85.38 + 20.780 -4.654 25.200
## m14 -392.60 74.88 + 2.214 -59.120 74.97
## m16 -578.20 108.20 + -0.645 -1.116 -5.925 -17.13
## log(mu.scl):sit log(k.scl):sit I(log(mu.scl)^2):sit
## m3
## m7 + +
## m1
## m6 +
## m5 +
## m9
## m11
## m0
## m2
## m13 + +
## m4
## m10
## m12
## m8 +
## m15 + + +
## m14 + +
## m16 + + +
## I(log(k.scl)^2):sit log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m3
## m7
## m1
## m6
## m5
## m9 51.660
## m11 38.000
## m0
## m2
## m13 7.902
## m4
## m10 51.520 -11.16
## m12 37.920 -59.41
## m8 +
## m15 -54.060
## m14 + 8.731 -28.60
## m16 + -55.590 73.20
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2
## m3
## m7
## m1
## m6
## m5
## m9
## m11 -14.22
## m0
## m2
## m13
## m4
## m10
## m12 -14.10 -39.31
## m8
## m15 -59.29
## m14
## m16 -62.10 98.39
## log(k.scl):log(mu.scl):sit I(log(k.scl)^2):log(mu.scl):sit
## m3
## m7
## m1
## m6
## m5
## m9
## m11
## m0
## m2
## m13 +
## m4
## m10
## m12
## m8
## m15 +
## m14 + +
## m16 + +
## I(log(mu.scl)^2):log(k.scl):sit I(log(k.scl)^2):I(log(mu.scl)^2):sit
## m3
## m7
## m1
## m6
## m5
## m9
## m11
## m0
## m2
## m13
## m4
## m10
## m12
## m8
## m15 +
## m14
## m16 + +
## df logLik AICc delta weight
## m3 10 -1213.757 2448.9 0.00 0.248
## m7 20 -1201.904 2449.5 0.58 0.185
## m1 9 -1215.370 2449.9 0.97 0.153
## m6 14 -1209.712 2450.2 1.25 0.133
## m5 14 -1210.223 2451.2 2.27 0.080
## m9 11 -1213.814 2451.3 2.40 0.074
## m11 13 -1212.041 2452.4 3.52 0.042
## m0 8 -1218.079 2453.1 4.15 0.031
## m2 9 -1217.246 2453.6 4.72 0.023
## m13 26 -1196.550 2455.0 6.08 0.012
## m4 10 -1217.083 2455.6 6.65 0.009
## m10 13 -1213.666 2455.7 6.77 0.008
## m12 16 -1211.795 2459.2 10.26 0.001
## m8 20 -1208.777 2463.2 14.32 0.000
## m15 38 -1186.079 2471.0 22.05 0.000
## m14 38 -1195.229 2489.3 40.35 0.000
## m16 56 -1183.374 2535.7 86.84 0.000
## Models ranked by AICc(x)
bestmod.turbid<-glm(log(turbidity.final)~log(maxvol)+site+log(mu.scalar)+I(log(mu.scalar)^2),family=gaussian, data = fulldata)
visreg(bestmod.turbid, "mu.scalar", by="site",ylab="Turbidity", overlay=FALSE, partial=TRUE, band=FALSE); #mu reduces turbid
aic.lmx(fulldata$oxygen.percent.final,gaussian, fulldata) #m5 log(mu)x site
## Model selection table
## (Int) log(mxv) sit log(mu.scl) log(k.scl) log(mu.scl)^2 log(k.scl)^2
## m5 25.32 -2.603 + -2.0860
## m1 23.38 -2.115 + 2.5290
## m3 21.61 -1.920 + 3.4860 0.8655
## m9 26.34 -2.614 + 2.5010 -1.3650
## m10 32.35 -3.190 + 4.3560 -1.3220 -9.85800
## m0 23.45 -2.209 +
## m4 30.72 -2.990 + -1.9330 -8.74300
## m2 28.13 -2.990 + -2.0110
## m11 23.40 -2.217 + 3.4600 0.2982 0.8478
## m12 27.24 -2.732 + 7.2210 0.2955 2.7280 -4.23400
## m7 22.54 -2.231 + -1.0280 0.8815
## m6 25.30 -2.440 + -5.3370
## m13 25.00 -2.417 + -1.6400 -6.2440
## m14 36.97 -4.618 + -0.8552 -6.4500 1.83800
## m8 30.60 -3.503 + -5.5440 2.70800
## m15 21.83 -2.007 + -0.1430 -5.9280 1.1760
## m16 36.28 -4.474 + -0.9257 -6.4070 -0.1708 -0.01162
## log(mu.scl):sit log(k.scl):sit I(log(mu.scl)^2):sit
## m5 +
## m1
## m3
## m9
## m10
## m0
## m4
## m2
## m11
## m12
## m7 + +
## m6 +
## m13 + +
## m14 + +
## m8 +
## m15 + + +
## m16 + + +
## I(log(k.scl)^2):sit log(k.scl):log(mu.scl) log(k.scl)^2):log(mu.scl
## m5
## m1
## m3
## m9 2.3970
## m10 2.4380 -5.708
## m0
## m4
## m2
## m11 0.3665
## m12 0.5110 -11.860
## m7
## m6
## m13 -3.1540
## m14 + -3.3830 -2.665
## m8 +
## m15 -4.5080
## m16 + -5.1360 3.082
## log(mu.scl)^2):log(k.scl log(k.scl)^2):I(log(mu.scl)^2
## m5
## m1
## m3
## m9
## m10
## m0
## m4
## m2
## m11 -1.7790
## m12 -1.7190 -5.922
## m7
## m6
## m13
## m14
## m8
## m15 -0.7258
## m16 -0.7566 3.695
## log(k.scl):log(mu.scl):sit I(log(k.scl)^2):log(mu.scl):sit
## m5
## m1
## m3
## m9
## m10
## m0
## m4
## m2
## m11
## m12
## m7
## m6
## m13 +
## m14 + +
## m8
## m15 +
## m16 + +
## I(log(mu.scl)^2):log(k.scl):sit I(log(k.scl)^2):I(log(mu.scl)^2):sit
## m5
## m1
## m3
## m9
## m10
## m0
## m4
## m2
## m11
## m12
## m7
## m6
## m13
## m14
## m8
## m15 +
## m16 + +
## df logLik AICc delta weight
## m5 12 -509.453 1045.6 0.00 0.445
## m1 8 -514.970 1047.1 1.56 0.204
## m3 9 -514.626 1048.8 3.18 0.091
## m9 10 -513.584 1049.0 3.44 0.080
## m10 12 -511.335 1049.3 3.76 0.068
## m0 7 -517.816 1050.6 4.98 0.037
## m4 9 -515.851 1051.2 5.63 0.027
## m2 8 -517.326 1051.8 6.27 0.019
## m11 12 -512.760 1052.2 6.61 0.016
## m12 15 -509.711 1053.6 8.06 0.008
## m7 17 -507.595 1054.7 9.08 0.005
## m6 12 -516.584 1059.8 14.26 0.000
## m13 22 -504.248 1062.0 16.38 0.000
## m14 32 -488.595 1063.0 17.39 0.000
## m8 17 -511.773 1063.0 17.44 0.000
## m15 32 -500.300 1086.4 40.80 0.000
## m16 47 -478.003 1105.0 59.46 0.000
## Models ranked by AICc(x)
bestmod.oxy<-glm(log(oxygen.percent.final)~log(maxvol)+site*log(mu.scalar),family=gaussian, data = fulldata)
visreg(bestmod.oxy, "mu.scalar", by="site",ylab="Oxygen", overlay=FALSE, partial=TRUE, band=FALSE); #mu reduces turbid
tapply(fulldata$oxygen.percent.final, fulldata$site, datacheck)#neither conc or percent in PR and CA
## argentina cardoso colombia costarica frenchguiana
## 21 0 20 30 29
## macae puertorico
## 30 0
#goes down with mu in Arg and Mac, but up in other three countries..?
#=====combined effects of taxonomy and hydrology?-----------
#=====multivariate analysis-------------------
fngrp<-select(fulldata, engulfer_bio, filter.feeder_bio, gatherer_bio, piercer_bio,scraper_bio,shredder_bio)
brom<-select(fulldata, site, change.mu, change.k, mu.scalar, k.scalar, intended.mu, intended.k, leaf.number,mean.diam,catchment.area, maxvol, cv.depth, prop.overflow.days,prop.driedout.days)
brom.min<-select(brom, site, mu.scalar, k.scalar, maxvol)
brom.med<-select(brom, site, mu.scalar, k.scalar, intended.mu, intended.k, maxvol)
brom.temptrue<-select(temphydrotruedata, site, change.mu, change.k, mu.scalar, k.scalar, intended.mu, intended.k, leaf.number,mean.diam,catchment.area, maxvol)
brom.noca<-select(nocadata, site, change.mu, change.k, mu.scalar, k.scalar, intended.mu, intended.k, leaf.number,mean.diam,catchment.area, maxvol)
hydro<-select(temphydrotruedata, cv.depth, prop.overflow.days,prop.driedout.days, mean.depth,long_dry,last_wet, change_cv_temp,change_mean_temp)
purehydro<-select(nocadata, cv.depth, prop.overflow.days,prop.driedout.days, mean.depth,long_dry,last_wet)
families<-select(fulldata, Calamoceratidae_bio,Candonidae_bio,Cecidomyiidae_bio,Ceratopogonidae_bio,Chironomidae_bio,
Coenagrionidae_bio,Corethrellidae_bio,Culicidae_bio,Dolichopodidae_bio, Dytiscidae_bio,
Empididae_bio,Enchytraeoidae_bio,Ephydridae_bio,Hirudinea,Hydrophilidae_bio,Lampyridae_bio,Limnocytheridae_bio,
Naididae_bio, Pseudostigmatidae_bio,Psychodidae_bio,Ptilodactylidae_bio,
Scirtidae_bio,Stratiomyidae_bio,Syrphidae_bio,Tabanidae_bio,TipulidaeLimoniidae_bio)
## Error in eval(expr, envir, enclos): object 'Hirudinea' not found
#I am not including Curculionidae_bio...leafminers
apply(families, 2, datacheck)
## Error in apply(families, 2, datacheck): object 'families' not found
#Excluded families with 5 or fewer bromeliads: Aeolosomatidae_bio (1), Anisopodidae_bio (5), Dolichopodidae_bio (5),
#Elateridae_bio (4), Elmidae_bio (1), Muscidae_bio (4), Periscelididae_bio (2), Phoridae_bio (2),
#Scatopsidae_bio (4), Sciaridae_bio (1), Sphaeroceridae_bio (5)
fn.names<-names(fngrp)
par(mfrow = c(1, 1))
fn.dist<-vegdist(fngrp)
fn.mds1<-metaMDS(fn.dist)
## Run 0 stress 0.1832603
## Run 1 stress 0.1842124
## Run 2 stress 0.1832606
## ... Procrustes: rmse 0.0001102766 max resid 0.0007837489
## ... Similar to previous best
## Run 3 stress 0.1962484
## Run 4 stress 0.1874409
## Run 5 stress 0.1832604
## ... Procrustes: rmse 2.45254e-05 max resid 0.0002326485
## ... Similar to previous best
## Run 6 stress 0.187681
## Run 7 stress 0.1832604
## ... Procrustes: rmse 4.601506e-05 max resid 0.0004592565
## ... Similar to previous best
## Run 8 stress 0.1842131
## Run 9 stress 0.1832603
## ... New best solution
## ... Procrustes: rmse 4.825723e-05 max resid 0.0006141904
## ... Similar to previous best
## Run 10 stress 0.1832605
## ... Procrustes: rmse 6.128458e-05 max resid 0.0005655417
## ... Similar to previous best
## Run 11 stress 0.1832609
## ... Procrustes: rmse 5.152246e-05 max resid 0.0006411468
## ... Similar to previous best
## Run 12 stress 0.1874396
## Run 13 stress 0.1832603
## ... New best solution
## ... Procrustes: rmse 3.865669e-05 max resid 0.0004750782
## ... Similar to previous best
## Run 14 stress 0.1869984
## Run 15 stress 0.1832603
## ... New best solution
## ... Procrustes: rmse 1.585839e-05 max resid 8.825876e-05
## ... Similar to previous best
## Run 16 stress 0.1842126
## Run 17 stress 0.1874396
## Run 18 stress 0.1832603
## ... Procrustes: rmse 1.247214e-05 max resid 9.424025e-05
## ... Similar to previous best
## Run 19 stress 0.1832606
## ... Procrustes: rmse 0.0001039098 max resid 0.001382091
## ... Similar to previous best
## Run 20 stress 0.1832603
## ... Procrustes: rmse 1.59092e-05 max resid 0.0001122416
## ... Similar to previous best
## *** Solution reached
ordiplot(fn.mds1)
## Warning in ordiplot(fn.mds1): Species scores not available
fnenv1<-envfit(fn.mds1, brom, permu = 999, na.rm=TRUE); fnenv1
##
## ***VECTORS
##
## NMDS1 NMDS2 r2 Pr(>r)
## change.mu 0.98269 -0.18523 0.0008 0.933
## change.k -0.31289 -0.94979 0.0102 0.402
## mu.scalar -0.15281 -0.98826 0.0016 0.862
## k.scalar 0.99642 -0.08449 0.0070 0.540
## intended.mu 0.09255 0.99571 0.0014 0.870
## intended.k -0.16162 0.98685 0.0495 0.012 *
## leaf.number -0.94054 0.33970 0.1287 0.001 ***
## mean.diam -0.49594 0.86836 0.0336 0.044 *
## catchment.area -0.25148 0.96786 0.2079 0.001 ***
## maxvol 0.40963 0.91225 0.0224 0.153
## cv.depth -0.73834 -0.67442 0.1072 0.001 ***
## prop.overflow.days 0.96995 0.24331 0.1190 0.001 ***
## prop.driedout.days -0.78399 -0.62077 0.0700 0.001 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Permutation: free
## Number of permutations: 999
##
## ***FACTORS:
##
## Centroids:
## NMDS1 NMDS2
## siteargentina -0.1337 -0.0910
## sitecolombia -0.1859 0.0591
## sitecostarica -0.1255 0.1704
## sitefrenchguiana 0.2114 -0.1119
## sitemacae -0.0322 0.0269
## sitepuertorico 0.1065 -0.1999
##
## Goodness of fit:
## r2 Pr(>r)
## site 0.4759 0.001 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Permutation: free
## Number of permutations: 999
##
## 30 observations deleted due to missingness
ordiplot(fn.mds1); plot(fnenv1)
## Warning in ordiplot(fn.mds1): Species scores not available
fnenv2<-envfit(fn.mds1, brom.med, permu = 999, na.rm=TRUE); fnenv2
##
## ***VECTORS
##
## NMDS1 NMDS2 r2 Pr(>r)
## mu.scalar -0.19565 -0.98067 0.0017 0.841
## k.scalar 0.97758 -0.21055 0.0031 0.730
## intended.mu -0.50977 0.86031 0.0006 0.939
## intended.k -0.57081 0.82109 0.0379 0.025 *
## maxvol 0.63163 0.77527 0.1392 0.001 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Permutation: free
## Number of permutations: 999
##
## ***FACTORS:
##
## Centroids:
## NMDS1 NMDS2
## siteargentina -0.1337 -0.0910
## sitecardoso 0.1595 0.1464
## sitecolombia -0.1859 0.0591
## sitecostarica -0.1255 0.1704
## sitefrenchguiana 0.2114 -0.1119
## sitemacae -0.0322 0.0269
## sitepuertorico 0.1065 -0.1999
##
## Goodness of fit:
## r2 Pr(>r)
## site 0.524 0.001 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Permutation: free
## Number of permutations: 999
ordiplot(fn.mds1); plot(fnenv2)
## Warning in ordiplot(fn.mds1): Species scores not available
fnenv3<-envfit(fn.mds1, brom.min, permu = 999, na.rm=TRUE); fnenv3
##
## ***VECTORS
##
## NMDS1 NMDS2 r2 Pr(>r)
## mu.scalar -0.19565 -0.98067 0.0017 0.824
## k.scalar 0.97758 -0.21055 0.0031 0.737
## maxvol 0.63163 0.77527 0.1392 0.001 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Permutation: free
## Number of permutations: 999
##
## ***FACTORS:
##
## Centroids:
## NMDS1 NMDS2
## siteargentina -0.1337 -0.0910
## sitecardoso 0.1595 0.1464
## sitecolombia -0.1859 0.0591
## sitecostarica -0.1255 0.1704
## sitefrenchguiana 0.2114 -0.1119
## sitemacae -0.0322 0.0269
## sitepuertorico 0.1065 -0.1999
##
## Goodness of fit:
## r2 Pr(>r)
## site 0.524 0.001 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Permutation: free
## Number of permutations: 999
ordiplot(fn.mds1); with(brom.min, ordiellipse(fn.mds1, site, kind = "se", conf = 0.95));plot(fnenv3)
## Warning in ordiplot(fn.mds1): Species scores not available
# fam.dist<-vegdist(families)
# fam.mds1<-metaMDS(fam.dist)
# ordiplot(fam.mds1)
# famenv1<-envfit(fam.mds1, brom, permu = 999, na.rm=TRUE); famenv1
# ordiplot(fam.mds1); plot(famenv1)
# famenv2<-envfit(fam.mds1, brom.med, permu = 999, na.rm=TRUE); famenv2
# ordiplot(fam.mds1); plot(famenv2)
#
# hydro.dist<-vegdist(hydro, method="euclidean")
#
# famenv3<-envfit(fam.mds1, brom.min, permu = 999, na.rm=TRUE); famenv3
# ordiplot(fam.mds1); with(brom.min, ordiellipse(fam.mds1, site, kind = "se", conf = 0.95)); plot(famenv3)
#
# protest(fn.mds1, fam.mds1) #r = 0.44, procrustes sumsq = 0.80, p =0.001
#
#
# #the following are anovas based on CCAs. These do not consider interactions between site and variables
# #so just looking at general effects
# fn.cca1 <- cca(fngrp ~ site+mu.scalar+k.scalar+maxvol+intended.k+intended.mu, brom); fn.cca1
# fn.cca1b <- cca(fngrp ~ site+log(mu.scalar)+log(k.scalar)+maxvol+log(intended.k)+log(intended.mu), brom)
# plot(fn.cca1); with(brom, ordiellipse(fn.cca1, site, kind = "se", conf = 0.95))
# anova(fn.cca1, by = "term", step=200) #just site sig with type 1 ss
# anova(fn.cca1, by = "margin", step=200)#site and k.scalar sig with type 3 ss
# anova(fn.cca1b, by = "margin", step=200)#site, logk, log intendedk
# plot(fn.cca1b)
# orditorp(fn.cca1b,dis="sp",labels=fn.names)
# plot(fn.cca1b, dis="sp")#mu and k cause shift from piercers to scrapers...
#
# fam.cca1 <- cca(families ~ site+mu.scalar+k.scalar+maxvol+intended.k+intended.mu, brom); fam.cca1
# plot(fam.cca1); with(brom, ordiellipse(fam.cca1, site, kind = "se", conf = 0.95))
# anova(fam.cca1, by = "term", step=200) #site and intended mu sig with type 1 ss
# anova(fam.cca1, by = "margin", step=200)#site, mu.scalar and intended.mu sig with type 3 ss
# # so one story is that fn composition follows k, whereas taxonomy follows mu
#
# #The following are CCAs with interactions, these cannot use type 3 (=marginal) tests
# fn.cca0 <- cca(fngrp ~ 1, brom);fn.cca0
# fn.cca0a <- cca(fngrp ~ log(maxvol)+site, brom);fn.cca0a #explains 44.85% of interia; 0.7588 of 1.3759 unexplained
# fn.cca0b <- cca(fngrp ~ log(maxvol)+site+log(mu.scalar)+log(k.scalar)+change.k+change.mu+log(mu.scalar):log(k.scalar)+change.mu:change.k, brom); fn.cca0b
# #without interactions, 46.69% of inertia explained, 0.7335 of 1.3759 unexplained
# fn.cca2 <- cca(fngrp ~ mu.scalar+k.scalar+maxvol+site+site:k.scalar+site:mu.scalar+mu.scalar:k.scalar, brom); fn.cca2
# fn.cca2b <- cca(fngrp ~ log(mu.scalar)+log(k.scalar)+maxvol+site+site:log(k.scalar)+site:log(mu.scalar)+log(mu.scalar):log(k.scalar), brom); fn.cca2b
# fn.cca2d <- cca(fngrp ~ log(mu.scalar)+log(k.scalar)+maxvol+site+site:log(k.scalar)+site:log(mu.scalar)+log(mu.scalar):log(k.scalar)+site:log(mu.scalar):log(k.scalar), brom); fn.cca2d
# fn.cca2e <- cca(fngrp ~ log(mu.scalar)+log(k.scalar)+change.k+change.mu+log(maxvol)+site+site:log(k.scalar)+site:log(mu.scalar)+log(mu.scalar):log(k.scalar)+site:log(mu.scalar):log(k.scalar)+site:change.k+site:change.mu+change.mu:change.k+site:change.mu:change.k, brom); fn.cca2e
# #so 0.5392 inertia unexplained...rainfall explains (0.7588-0.5392)/(0.7588) = 28.9% of non-site effect; of this rainfall effect (0.7335-0.5392)/(0.7588-0.5392)=88% is explained by contingency
# plot(fn.cca2b); with(brom, ordiellipse(fn.cca2b, site, kind = "se", conf = 0.95))
# anova(fn.cca2, by = "term", step=200)#only site and maxvol with type 1, type 3 ignores main effects
# anova(fn.cca2b, by = "term", step=1000)#site, maxvol, logk p =0.045 (this is jumping around 0.05)
# anova(fn.cca2d, by = "term", step=1000)#site, maxvol, log k p = 0.048 (this is jumping around 0.05)
# anova(fn.cca2e, by = "term", step=1000)#site, maxvol, absolute change in k from 1 x site, explains 60% of inertia
# mod <- step(fn.cca0, scope = formula(fn.cca2e), test = "perm")
# fn.cca2c <- cca(fngrp ~ log(k.scalar)+maxvol+site, brom); fn.cca2c
# anova(fn.cca2c, by = "term", step=200)
# plot(fn.cca2c); with(brom, ordiellipse(fn.cca2c, site, kind = "se", conf = 0.95)); text(fn.cca2c,dis="sp",labels=fn.names)
# fn.cca2f <- cca(fngrp ~ log(maxvol)+site+site:change.k, brom); anova(fn.cca2f, by = "term", step=1000)#simplified model, all very sig
# #this plot shows that PR plus log K both select for lots of shredders; opposing this trend are the engulfers,gatherers plus other sites
# par(mfrow=c(1,1))
# plot(fn.cca2f); with(brom, ordiellipse(fn.cca2f, site, kind = "se", conf = 0.95)); text(fn.cca2f,dis="sp",labels=fn.names)
#
# scl <- 3 ## scaling = 3
# colvec <- c("red2", "green4", "mediumblue", "tan4", "plum4", "lawngreen", "cyan")
# plot(fn.cca2f, type="n", scaling = scl)
# with(brom, points(fn.cca2f, display = "sites", col = colvec[site],
# scaling = scl, pch = 21, bg = colvec[site]))
# points(fn.cca2f, display = "bp", scaling=scl)
# with(brom, text(fn.cca2f,dis="sp",labels=fn.names, cex=0.5))
# with(brom, legend("bottomleft", legend = levels(site), bty = "n",cex=0.5,
# col = colvec, pch = 21, pt.bg = colvec))
#
#
#
#
# fam.cca2 <- cca(families ~ mu.scalar+k.scalar+maxvol+site+site:k.scalar+site:mu.scalar+mu.scalar:k.scalar, brom); fam.cca2
# fam.cca2b <- cca(families ~ log(mu.scalar)+log(k.scalar)+maxvol+site+site:log(k.scalar)+site:log(mu.scalar)+log(mu.scalar):log(k.scalar), brom); fam.cca2b
# plot(fam.cca2b); with(brom, ordiellipse(fam.cca2, site, kind = "se", conf = 0.95))
# anova(fam.cca2, by = "term", step=200)#maxvol, site, mu.scalar x site
# anova(fam.cca2b, by = "term", step=200)#maxvol, site, logmu.scalar x site
# fam.cca2c <- cca(families ~ maxvol+site+log(mu.scalar):site, brom)
# anova(fam.cca2c, by = "term", step=200)
# fam.cca2d <- cca(families ~ log(mu.scalar)+log(k.scalar)+maxvol+site+site:log(k.scalar)+site:log(mu.scalar)+log(mu.scalar):log(k.scalar)+site:log(mu.scalar):log(k.scalar), brom); fam.cca2d
# anova(fam.cca2d, by = "term", step=200)#maxvol, site, logmu.scalar x site
# fam.cca2e <- cca(families ~ log(mu.scalar)+log(k.scalar)+change.k+change.mu+maxvol+site+site:log(k.scalar)+site:log(mu.scalar)+log(mu.scalar):log(k.scalar)+site:log(mu.scalar):log(k.scalar)+site:change.k+site:change.mu+change.mu:change.k+site:change.mu:change.k, brom); fam.cca2e
# anova(fam.cca2e, by = "term", step=200)
# fam.cca2f <- cca(families ~ maxvol+site+site:change.k+site:log(mu.scalar), brom);
# anova(fam.cca2f, by = "term", step=200)#all sig
#
# scl <- 3 ## scaling = 3
# colvec <- c("red2", "green4", "mediumblue", "tan4", "plum4", "lawngreen", "cyan")
# plot(fam.cca2c, type="n", scaling = scl)
# with(brom, points(fam.cca2c, display = "sites", col = colvec[site],
# scaling = scl, pch = 21, bg = colvec[site]))
# points(fam.cca2c, display = "bp", scaling=scl)
# with(brom, legend("bottomleft", legend = levels(site), bty = "n",cex=0.5,
# col = colvec, pch = 21, pt.bg = colvec))
#
# scl <- 3 ## scaling = 3
# colvec <- c("red2", "green4", "mediumblue", "tan4", "plum4", "lawngreen", "cyan")
# plot(fam.cca2f, type="n", scaling = scl)
# with(brom, points(fam.cca2f, display = "sites", col = colvec[site],
# scaling = scl, pch = 21, bg = colvec[site]))
# points(fam.cca2f, display = "bp", scaling=scl)
# with(brom, legend("bottomleft", legend = levels(site), bty = "n",cex=0.5,
# col = colvec, pch = 21, pt.bg = colvec))
#
# demo<-plot(fam.cca2c, display=c("cn","bp", "sites"), cex=0.5); with(brom, ordiellipse(fam.cca2c, site, kind = "se", conf = 0.95)); orditorp(fam.cca2c,dis="sp")
# #identify(demo, "biplot")
#
# #this loosely agrees with adonis that follows
#
# #use adonis2 for marginal term testing but tosses out main effects, adonis uses sequential
#
# fn.dist<-vegdist(fngrp, method="horn")
#
# fn.adon1b<-adonis(fn.dist~log(mu.scalar)+log(k.scalar)+maxvol+site+site:log(k.scalar)+site:log(mu.scalar)+log(mu.scalar):log(k.scalar), by="margin", brom, perm = 200)
# fn.adon1b #maxvol, site, log k is 0.02
#
# fn.adon1c<-adonis(fn.dist~log(mu.scalar)+log(k.scalar)+maxvol+site+site:log(k.scalar)+site:log(mu.scalar)+log(mu.scalar):log(k.scalar)+site:log(mu.scalar):log(k.scalar), by="margin", brom, perm = 200)
# fn.adon1c#maxvol, site, log k is 0.054
#
# fn.adon1d<-adonis(fn.dist~log(k.scalar)+maxvol+site, by="margin", brom, perm = 200)
# fn.adon1d#maxvol, site, logk is p =0.049, #so, like the cca, the effect of log k is bouncing around sig
#
# fn.adon1e<-adonis(fn.dist~log(mu.scalar)+log(k.scalar)+log(maxvol)+site+site:log(k.scalar)+site:log(mu.scalar)+log(mu.scalar):log(k.scalar)+site:log(mu.scalar):log(k.scalar)+change.k+change.mu+site:change.k+site:change.mu+change.mu:change.k+site:change.mu:change.k, by="margin", brom, perm = 200)
# fn.adon1e#maxvol, site, log k is 0.05, oddly change.k not sig here unlike cca
#
# fn.adon1e2<-adonis(fn.dist~log(k.scalar)+log(maxvol)+site+site:log(k.scalar)+change.k+site:change.k, by="margin", brom, perm = 1000)
# fn.adon1e2#site and log k, (site x change k is at p=0.11)
#
# fn.adon1f<-adonis(fn.dist~log(maxvol)+site, by="margin", brom, perm = 200)
# fn.adon1f #so 29.79 of the 52.43 sunsq are unexplained, model fn.adon1e left unexplained 23.284, so (29.79-23.284)/ 29.79 = 21.8%
#
# fn.adon1g<-adonis(fn.dist~log(maxvol)+site+log(mu.scalar)+log(k.scalar)+log(mu.scalar):log(k.scalar)+change.k+change.mu+change.mu:change.k, by="margin", brom, perm = 200)
# fn.adon1g #here 28.875 unexplained, so general effects of rainfall explain (29.79 - 28.875)/(29.79-23.284) = 14%, and contingent explain (28.875-23.284)/(29.79-23.284) = 86% of this diff
#
# fam.adon1<-adonis(fam.dist~mu.scalar+k.scalar+maxvol+site+site:k.scalar+site:mu.scalar+mu.scalar:k.scalar, by="margin", brom, perm = 200)
# fam.adon1#maxvol, site, k*site. mu*site
#
# fam.adon1b<-adonis(fam.dist~log(mu.scalar)+log(k.scalar)+maxvol+site+site:log(k.scalar)+site:log(mu.scalar)+log(mu.scalar):log(k.scalar), by="margin", brom, perm = 200)
# fam.adon1b#maxvol, site, logk, logmu*site (logk*site is 0.055)
#
# fam.adon1c<-adonis(fam.dist~log(mu.scalar)+log(k.scalar)+maxvol+site+site:log(k.scalar)+site:log(mu.scalar)+log(mu.scalar):log(k.scalar)+site:log(mu.scalar):log(k.scalar), by="margin", brom, perm = 200)
# fam.adon1c#maxvol, site, logk, logmu*site (logk*site is 0.055) use this model
#
# fam.adon2<-adonis(fam.dist~mu.scalar+k.scalar+maxvol, strata = brom$site, brom, by="margin", perm = 200)
# fam.adon2#confirming above, if we restrict permutations to be within site, there is then no overall effect of mu or k (just maxvol)
#
# #so another story is that contingency in taxonomic composition response doesnt extend to functional composition
# #both adonis and the cca suggest that k marginally affects fn composition, and both mu and k additionally affect family comp but mu effects are contingent.
#
# mantel(fn.dist, fam.dist, na.rm=TRUE) #r=0.86, p = 0.001
#
# #hydrology multivariate
#
# hydro.cca <- cca(hydro ~ log(mu.scalar)+log(k.scalar)+change.k+change.mu+maxvol+site+site:log(k.scalar)+site:log(mu.scalar)+log(mu.scalar):log(k.scalar)+site:log(mu.scalar):log(k.scalar)+site:change.k+site:change.mu+change.mu:change.k+site:change.mu:change.k, brom.temptrue)
# anova(hydro.cca, by = "term", step=200) #overwhelmingly log k (not even site!)
#
# hydro.cca2 <- cca(hydro ~ log(mu.scalar)+log(k.scalar)+maxvol+site+site:log(k.scalar)+site:log(mu.scalar)+site:change.k+site:change.mu+intended.mu+intended.k, brom.temptrue)
# anova(hydro.cca2, by = "term", step=200) #overwhelmingly log k (not even site!)
#
#
# #===hydrology on composition
# fngrp2<-select(fulldata, site.x, engulfer_bio, filter.feeder_bio, gatherer_bio, piercer_bio,scraper_bio,shredder_bio)
# bromnoca<-filter(brom, site!="cardoso")%>%select(-leaf.number)
# fngrpnoca<-filter(fngrp2, site.x!="cardoso")%>%select(-site.x)
# fn.cca2e <- cca(fngrpnoca ~ maxvol+site+log(mu.scalar)+log(k.scalar)+prop.driedout.days+prop.overflow.days+cv.depth, bromnoca); fn.cca2e
# anova(fn.cca2e, by = "term", step=200)#amazing, no hydrology sig just maxvol, site, marg k (p=0.08)
# plot(fn.cca2e);text(fn.cca2e,dis="sp",labels=fn.names)
# #note that mu and k and overflow go in one direction, and driedout and cvdepth in the other, as expected
# #here, increasing mu and k associated with scrapers, and away from piercers
#
# #====separate fn grps===multivariate==
# namedata<-read.csv( "C:/Users/Diane/Dropbox/BWG Drought experiment/Paper 1_thresholds/Data/bwgdb_names.csv")
#
# eng_family<-as.data.frame(namedata$family[namedata$functional_group=="engulfer"])
# eng_fam<-as.factor(unique(eng_family[,1]))
# eng_bio<-select(fulldata, Dytiscidae_bio, Corethrellidae_bio,
# Hydrophilidae_bio, Coenagrionidae_bio, Pseudostigmatidae_bio, Hirudinea,Toxorhynchites)
#
# #*Axymyiidae removed as did not occur in drought exp,
# #Dolichopodidae_bio, Periscelididae_bio removed as in 5 or fewer bromeliads
# #Glossiphoniidae is removed as rarely identified as a family of Hirudinea (subclass)
# #Muscidae removed as 6 records cannot confidently be attributed to engulfing pred
#
# gath_family<-as.data.frame(namedata$family[namedata$functional_group=="gatherer"])
# gath_fam<-as.factor(unique(gath_family[,1]))
# gath_bio<-select(fulldata, detCerato_bio,Ephydridae_bio,
# Psychodidae_bio, Stratiomyidae_bio, Syrphidae_bio, Enchytraeoidae_bio,
# Naididae_bio, detChiron_bio)
#
# #removed aff. Drosophilidae, Aulacigastridae (honduras), Lumbricidae, Entomobryidae as not in drought exp, and latter terrestrial
# #removed Anisopodidae_bio,Sciaridae_bio, Phoridae_bio, Scatopsidae_bio, Sphaeroceridae_bio,
# #consider using Oligochaeta_bio column (subclass) instead of Enchytraeoidae and Naididae as families within (as most not id to families)
#
# pier_family<-as.data.frame(namedata$family[namedata$functional_group=="piercer"])
# pier_fam<-as.factor(unique(pier_family[,1]))
# pier_bio<-select(fulldata, Cecidomyiidae_bio, predCerato_bio, Empididae_bio, Lampyridae_bio, Tabanidae_bio, Tanypodinae_bio)
#
# #Veliidae/Vellidae, Gerridae, Mesoveliidae, Enicocephalidae (dominica) removed as not in drought exp,
# #changed chironomidae to tanypodinae
# #Hydrophilidae_bio removed as piercer, as most classified as engulfers in database
#
# #note Canacidae (diptera.529) occurred in cardoso drought exp but not uploaded as a family - why?
# #need to add Bezzia/Sphaeromias/Stilobezzia/Culicoides (genus) columns for pred ceratos.
#
# filt_family<-as.data.frame(namedata$family[namedata$functional_group=="filter.feeder"])
# filt_fam<-as.factor(unique(filt_family[,1]))
# filt_bio<-select(fulldata, Anopheles, Wyeomyia, Culex)
# #Canthocamptidae, Daphnidae/Daphniidae, Cyclopidae removed as crustaceans, Dixidae as not in drought expt
#
# shred_family<-as.data.frame(namedata$family[namedata$functional_group=="shredder"])
# shred_fam<-as.factor(unique(shred_family[,1]))
# shred_bio<-select(families, TipulidaeLimoniidae_bio, Ptilodactylidae_bio,Calamoceratidae_bio)
#
# scrap_family<-as.data.frame(namedata$family[namedata$functional_group=="scraper"])
# scrap_fam<-as.factor(unique(scrap_family[,1]))
# scrap_bio<-select(families, Scirtidae_bio,Candonidae_bio,Limnocytheridae_bio)
# #removed Thaumaleidae (colombia), Cyprididae (cardoso) as not in drought expt
# #Elmidae_bio is five or fewer bromeliads
#
# ##analysis==
#
# eng_bio$eng_sum<-rowSums(eng_bio)
# brom$eng_sum<-rowSums(eng_bio)
# eng_bio_nozero<-filter(eng_bio,eng_sum>0)%>%select(-eng_sum)
# eng.dist<-vegdist(eng_bio_nozero)
# adonis(eng.dist~log(mu.scalar)+log(k.scalar)+maxvol+site+site:log(k.scalar)+site:log(mu.scalar)+log(mu.scalar):log(k.scalar), by="margin", filter(brom,eng_sum>0), perm = 1000)
# #maxvol, site, log k*site, log mu *site, marginally log mu (shows contingency as in regressions)
# mean(eng.dist)#0.803
#
# gath_bio$gath_sum<-rowSums(gath_bio)
# brom$gath_sum<-rowSums(gath_bio)
# gath_bio_nozero<-filter(gath_bio,gath_sum>0)%>%select(-gath_sum)
# gath.dist<-vegdist(gath_bio_nozero)
# adonis(gath.dist~log(mu.scalar)+log(k.scalar)+maxvol+site+site:log(k.scalar)+site:log(mu.scalar)+log(mu.scalar):log(k.scalar), by="margin", filter(brom,gath_sum>0), perm = 1000)
# #maxvol, site, logmu x site (now agrees with regr analysis, where both gatherer and chironmid biomass conting and responsive to rain)
# mean(gath.dist)#0.793
#
#
#
# pier_bio$pier_sum<-rowSums(pier_bio)
# brom$pier_sum<-rowSums(pier_bio)
# pier_bio_nozero<-filter(pier_bio,pier_sum>0)%>%select(-pier_sum)
# pier.dist<-vegdist(pier_bio_nozero)
# adonis(pier.dist~log(mu.scalar)+log(k.scalar)+maxvol+site+site:log(k.scalar)+site:log(mu.scalar)+log(mu.scalar):log(k.scalar), by="margin", filter(brom,pier_sum>0), perm = 1000)
# #maxvol, site only (agrees with regr analysis, piercer biomass not contingent or responsive to rain)
# mean(pier.dist)#0.842
#
#
# filt_bio$filt_sum<-rowSums(filt_bio)
# brom$filt_sum<-rowSums(filt_bio)
# filt_bio_nozero<-filter(filt_bio,filt_sum>0)%>%select(-filt_sum)
# filt.dist<-vegdist(filt_bio_nozero)
# adonis(filt.dist~log(mu.scalar)+log(k.scalar)+maxvol+site+site:log(k.scalar)+site:log(mu.scalar)+log(mu.scalar):log(k.scalar), by="margin", filter(brom,filt_sum>0), perm = 1000)
# #maxvol, site, log mu (marg log mu x site, p =0.052)
# mean(filt.dist)#0.666
#
# scrap_bio$scrap_sum<-rowSums(scrap_bio)
# brom$scrap_sum<-rowSums(scrap_bio)
# scrap_bio_nozero<-filter(scrap_bio,scrap_sum>0)%>%select(-scrap_sum)
# scrap.dist<-vegdist(scrap_bio_nozero)
# adonis(scrap.dist~log(mu.scalar)+log(k.scalar)+maxvol+site+site:log(k.scalar)+site:log(mu.scalar)+log(mu.scalar):log(k.scalar), by="margin", filter(brom,scrap_sum>0), perm = 1000)
# #maxvol, site, log k x site (agrees with regr analysis, both scraper and scirtid biomass contingent)
# mean(scrap.dist)#0.650
#
# shred_bio$shred_sum<-rowSums(shred_bio)
# brom$shred_sum<-rowSums(shred_bio)
# shred_bio_nozero<-filter(shred_bio,shred_sum>0)%>%select(-shred_sum)
# shred.dist<-vegdist(shred_bio_nozero)
# adonis(shred.dist~log(mu.scalar)+log(k.scalar)+maxvol+site+site:log(k.scalar)+site:log(mu.scalar)+log(mu.scalar):log(k.scalar), by="margin", filter(brom,shred_sum>0), perm = 1000)
# #max vol, site only (agrees with regr analysis, neither shredder or tipulid biomass contingent nor that responsive to rain)
# mean(shred.dist)#0.557
#
# #===sensitivity
#
# #restricted to sites with family present in at least 25% (8 or more) bromeliads
#
# n<-21
# sum.outa<-data.frame(full.d2=numeric(n),noint.d2=numeric(n), intend.d2=numeric(n), site.d2=numeric(n), conting.p=numeric(n),general.p=numeric(n), siterain.p=numeric(n), rainfall.p=numeric(n),sensitive.p=numeric(n) )
# rowName<-c(1:21)
# sum.outa[1,]<-allmodel(2,noargprdata$Ceratopogonidae_bio,poisson, noargprdata); rowName[1]<-"Ceratopogonidae_bio"#not AR, PR, rest >11/site
# sum.outa[2,]<-allmodel(2,nofgdata$Chironomidae_bio,poisson, nofgdata); rowName[2]<-"Chironomidae_bio"#not FG, rest >15/site
# sum.outa[3,]<-allmodel(2,camadata$Coenagrionidae_bio,poisson, camadata); rowName[3]<-"Coenagrionidae_bio"#not CR, PR, AR, barely FG (2) CO (4), but CA and MA >18/site
# sum.outa[4,]<-allmodel(2,cafgmadata$Corethrellidae_bio,poisson, cafgmadata); rowName[4]<-"Corethrellidae_bio"#only CA, FG, MA, all >24/site
# sum.outa[5,]<-allmodel(2,nocodata$Culicidae_bio,poisson, nocodata); rowName[5]<-"Culicidae_bio"#all but CO =5, and CR =9; rest >14/site
# sum.outa[6,]<-allmodel(2,camadata$Empididae_bio,poisson, camadata); rowName[6]<-"Empididae_bio"# Cardoso and Macae >16/site
# sum.outa[7,]<-allmodel(2,noargcocrdata$Limnocytheridae_bio,poisson, noargcocrdata); rowName[7]<-"Limnocytheridae_bio"#use CA, FG, MA, PR >14
# sum.outa[8,]<-allmodel(2,noargcodata$Naididae_bio,poisson, noargcodata); rowName[8]<-"Naididae_bio"# use CA, CR, FG, MA, PR
# sum.outa[9,]<-allmodel(2,argcacodata$Psychodidae_bio,poisson, argcacodata); rowName[9]<-"Psychodidae_bio"#use AR, CA, CO (>14/site) but not CR=5 or MA=3
# sum.outa[10,]<-allmodel(2,fulldata$Scirtidae_bio,poisson, fulldata); rowName[10]<-"Scirtidae_bio"#all, >28
# sum.outa[11,]<-allmodel(2,cocrdata$Syrphidae_bio,poisson, cocrdata); rowName[11]<-"Syrphidae_bio"#Use CO=11, CR=13; not AR=4, CA=6, MA=3
# sum.outa[12,]<-allmodel(2,argcrdata$Tabanidae_bio,poisson, argcrdata); rowName[12]<-"Tabanidae_bio"#Use AR=18, CR=7, not CA=5, MA=5, FG=1
# sum.outa[13,]<-allmodel(2,fulldata$TipulidaeLimoniidae_bio,poisson, fulldata); rowName[13]<-"TipulidaeLimoniidae_bio"#Use all >18
# sum.outa[14,]<-allmodel(2,noargcodata$Oligochaeta,poisson, noargcodata); rowName[14]<-"Oligochaeta"#cardoso, cr,fg, ma, pr all >17, 0 in cr, ar
# sum.outa[15,]<-allmodel(2,camaprdata$Tanypodinae_bio,poisson, camaprdata); rowName[15]<-"Tanypodinae_bio"#cardoso 30, macae 23, pr 8
# sum.outa[16,]<-allmodel(2,cafgdata$predCerato_bio,poisson, cafgdata); rowName[16]<-"predCerato_bio"#cardoso 22, fg 28
# sum.outa[17,]<-allmodel(2,cacrmadata$detCerato_bio,poisson, cacrmadata); rowName[17]<-"detCerato_bio"#ca, cr, ma
# sum.outa[18,]<-allmodel(2,nocodata$Culex,poisson, nocodata); rowName[18]<-"Culex"#all but Colombia
# sum.outa[19,]<-allmodel(2,noargcacodata$Wyeomyia,poisson, noargcacodata); rowName[19]<-"Wyeomyia"#not arg, col, and only 2 in cardoso
# sum.outa[20,]<-allmodel(2,nocodata$filtCulicidae,poisson, nocodata); rowName[20]<-"filtCulicidae"#all but colombia >9
# sum.outa[21,]<-allmodel(2,nofgdata$detChiron_bio,poisson, nofgdata); rowName[21]<-"detChiron_bio"#all but fg
#
# row.names(sum.outa)<-rowName
# sum.outa$taxa<-rowName
# sum.outa$rain.percent<-100*(sum.outa$full.d2-sum.outa$site.d2)/(1-sum.outa$site.d2)
# sum.outa$conting.percent<-100*(sum.outa$full.d2-sum.outa$noint.d2)/(1-sum.outa$site.d2)
# sum.outa$general.percent<-100*(sum.outa$noint.d2-sum.outa$site.d2)/(1-sum.outa$site.d2)
#
# t1<-knitr::kable(sum.outa)
#
# #these in 8 or more bromeliads in only one site, so no site term in model
# #need to write a new function without site!
# n<-11
# sum.outb<-data.frame(full.d2=numeric(n),maxvol.d2=numeric(n),sensitive.p=numeric(n) )
# rowName<-c(1:11)
# sum.outb[1,]<-simplemodel(2,cadata$Calamoceratidae_bio,poisson, cadata); rowName[1]<-"Calamoceratidae_bio"#Only Cardoso: 19 brom
# sum.outb[2,]<-simplemodel(2,prdata$Candonidae_bio,poisson,prdata); rowName[2]<-"Candonidae_bio"#Only puerto rico: 27 brom
# sum.outb[3,]<-simplemodel(2,fgdata$Cecidomyiidae_bio,poisson, fgdata); rowName[3]<-"Cecidomyiidae_bio"#almost entirely FG: 17 brom (1-2 in CR, CA)
# sum.outb[4,]<-simplemodel(2,prdata$Enchytraeoidae_bio,poisson, prdata); rowName[4]<-"Enchytraeoidae_bio"#only PR (>17)
# sum.outb[5,]<-simplemodel(2,crdata$Hydrophilidae_bio,poisson, crdata); rowName[5]<-"Hydrophilidae_bio"# poss CR =8, not Cardoso = 1, MA = 4
# sum.outb[6,]<-simplemodel(2,crdata$Pseudostigmatidae_bio,poisson, crdata); rowName[6]<-"Pseudostigmatidae_bio"# poss CR =8
# sum.outb[7,]<-simplemodel(2,cadata$Stratiomyidae_bio,poisson, cadata); rowName[7]<-"Stratiomyidae_bio"#use CA =13, not CO=4
# sum.outb[8,]<-simplemodel(2,cadata$Ephydridae_bio,poisson, cadata); rowName[8]<-"Ephydridae_bio"#use Cardoso (29), not MA=1
# sum.outb[9,]<-simplemodel(2,cadata$Dytiscidae_bio,poisson, cadata); rowName[9]<-"Dytiscidae_bio"#Only Cardoso (10), with FG=3, MA=1
# sum.outb[10,]<-simplemodel(2,cadata$Anopheles,poisson, cadata); rowName[10]<-"Anopheles"#only cardoso (9) and fg (4)
# sum.outb[11,]<-simplemodel(2,cadata$Hirudinea,poisson, cadata); rowName[11]<-"Hirudinea"#only Cardoso 17, ar and cr only 1
# row.names(sum.outb)<-rowName
# sum.outb$taxa<-rowName
#
# sum.outb$rain.percent<-100*(sum.outb$full.d2-sum.outb$maxvol.d2)/(1-sum.outb$maxvol.d2)
# sum.outb$conting.percent<-0
# sum.outb$general.percent<-0
#
# t2<-knitr::kable(sum.outb)
#
# #these did not occur in enough (8 or more) bromeliads in any one site for analysis:
# #"Lampyridae_bio": Ca and Ma both 3
# #"Ptilodactylidae_bio":CO=6
# #Toxorhynchites, none enough
#
# sumoutc<-full_join(sum.outa, sum.outb)
# #sumoutc[is.na(sumoutc)] <- 0
#
# #note that these conting/general calc are only accurate for familes that occur in more than one site, otherwise should be ignored
#
# overall.percent[1,1]<-mean(sumoutc$rain.percent[1:20])#0.42 +/- 0.02(SE)
# overall.percent[1,2]<-mean(sumoutc$conting.percent[1:20])#0.24
# overall.percent[1,3]<-mean(sumoutc$general.percent[1:20])#0.18
# overall.percent[1,4]<-sd(sumoutc$rain.percent[1:20])/(20^0.5)
# overall.percent[1,5]<-sd(sumoutc$conting.percent[1:20])/(20^0.5)
# overall.percent[1,6]<-sd(sumoutc$general.percent[1:20])/(20^0.5)
#
# kable(sumoutc[,c(5,6,9,10,12)]) #note that when overall rainfall affects >38% of non-site deviance, then sensitive.p is sig
# #these seems robust to whether the model is multi-site or single-site...yay!
#
#
#
#
#
# taxa<-c("Coenagrionidae_bio", "Corethrellidae_bio", "Empididae_bio", "Limnocytheridae_bio","Naididae_bio", "Psychodidae_bio", "Scirtidae_bio",
# "Syrphidae_bio","Tabanidae_bio", "TipulidaeLimoniidae_bio", "Tanypodinae_bio","predCerato_bio", "detCerato_bio",
# "filtCulicidae", "detChiron_bio", "Calamoceratidae_bio", "Candonidae_bio", "Cecidomyiidae_bio", "Enchytraeoidae_bio", "Hydrophilidae_bio",
# "Pseudostigmatidae_bio","Stratiomyidae_bio", "Ephydridae_bio", "Dytiscidae_bio", "Hirudinea")
# functaxa<-as.data.frame(taxa)
# functaxa$functional.group<-c("Engulfer", "Engulfer","Piercer", "Scraper", "Gatherer", "Gatherer", "Scraper",
# "Gatherer", "Piercer", "Shredder", "Piercer", "Piercer", "Gatherer",
# "Filter feeder", "Gatherer", "Shredder", "Scraper", "Piercer", "Gatherer", "Engulfer",
# "Engulfer", "Gatherer", "Gatherer", "Engulfer", "Engulfer")
#
# world_biomass<-select(fulldata, one_of(taxa))%>% apply(2, sum)%>%as.data.frame()
#
# colnames(world_biomass)<-"allbiomass"
# world_biomass$taxa<-rownames(world_biomass)
#
# sumoutd<-left_join(functaxa, sumoutc)%>%left_join(world_biomass)
#
# sum.oute<-sum.out[1:6,]
# sum.oute$functional.group<-c("Shredder", "Filter feeder", "Scraper", "Gatherer", "Engulfer", "Piercer")
#
# ggplot(sumoutd, aes(x = functional.group, y = rain.percent, label = taxa)) +
# geom_point(aes(size = allbiomass, colour = sensitive.p),alpha=.2) +
# geom_text(hjust = 1, size = 3) +
# scale_size(range = c(1,15)) +
# theme_bw()
#
#
# sumoutf<-sum.oute%>%select(functional.group, conting.percent,general.percent)%>% gather(Source, Deviance_explained, conting.percent:general.percent)
#
#
# sumoutf$functional.group <- factor(sumoutf$functional.group, levels = c("Engulfer", "Filter feeder", "Gatherer",
# "Scraper", "Piercer", "Shredder"))
#
# ggplot() +
# geom_bar(data=sumoutf, aes(y=Deviance_explained,x=functional.group, fill=Source), stat="identity", width = 0.75) +
# geom_point(data=sumoutd, aes(x = functional.group, y = rain.percent, size = allbiomass),alpha=.5) +
# geom_text(hjust = 1, size = 3) +
# scale_size(range = c(1,15)) +
# labs(title = "Effect of rainfall changes on invertebrate families and functional groups",
# y = "Proportion residual deviance explained by rainfall", x = "Functional group", fill = "Type of rainfall effect")+
# theme_bw()
#
# #overall figure
# overall.percent$response<-factor(overall.percent$response, levels = c("Invertebrate family", "Functional group biomass", "Total invertebrate biomass", "Bacterial density", "Ecosystem functions"))
# stacked.percent<-overall.percent %>% gather(type, percent, conting:general)
#
#
# dodge <- position_dodge(width = 0.9)
# limits<-aes(ymax = overall.percent$rain + overall.percent$se.rain, ymin=overall.percent$rain - overall.percent$se.rain)
#
# ggplot(data = overall.percent, aes(x = response, y = rain))+
# geom_errorbar(limits, position = dodge, width = 0.25) +
# geom_bar(data=stacked.percent, aes(y=percent,x=response, fill=type), stat="identity", width = 0.75)+
# labs(title = "Effect of rainfall changes on responses",
# y = "Proportion residual deviance explained by rainfall", x = "Response type")
#
# #repeat family analysis with aic-c model selection
#
# aic.lmxnb(round(noargprdata$Ceratopogonidae_bio*100), noargprdata)#m9 (site+mu x k), m11
# aic.lmxnb(round(nofgdata$Chironomidae_bio*100), nofgdata)#m5 (site * mu) m3
#
# aic.lmxnb(round(camadata$Coenagrionidae_bio*10), camadata)#needs starting values try Theta = 0.3479 , m16 (site*k+k2*mu+mu2) by poisson
# aic.lmxnb.init(round(camadata$Coenagrionidae_bio*10), 0.3479, camadata)#m8
# aic.lmx(round(camadata$Coenagrionidae_bio*10), family=negative.binomial(theta = 0.3479), camadata)#m8 (site x (k+k2))
#
# aic.lmxnb(round(cafgmadata$Corethrellidae_bio*100), cafgmadata)#m9 (site+mu x k)
# aic.lmxnb(round(nocodata$Culicidae_bio*100), nocodata)#m7 (site*mu+mu2)
# aic.lmxnb(round(camadata$Empididae_bio*100), camadata)#m7 (site*mu+mu2) m5
# aic.lmxnb(round(noargcocrdata$Limnocytheridae_bio*100), noargcocrdata)#m6 (site x k)
# aic.lmxnb(round(noargcodata$Naididae_bio*100), noargcodata) #m0 (no rainfall effect)
#
# aic.lmxnb(round(argcacodata$Psychodidae_bio*100), argcacodata)#needs starting values try Theta: 0.2222
# aic.lmxnb.init(round(argcacodata$Psychodidae_bio*100),0.2222, argcacodata)#m0 but lots didnt converge
# aic.lmx(round(argcacodata$Psychodidae_bio*100), family=negative.binomial(theta = 0.2222), argcacodata)#m0 (no rainfall effect)
#
# aic.lmxnb(round(fulldata$Scirtidae_bio*100), fulldata)#m8 m0 m2 m6 (no rainfall effect)
#
# aic.lmxnb(round(cocrdata$Syrphidae_bio*100), cocrdata)#m16 but model had troubles convergings, NaNs
# #nope aic.lmxnb.init(round(cocrdata$Syrphidae_bio*100), 0.0957,cocrdata)
# # nope aic.lmx(round(cocrdata$Syrphidae_bio*100), family=negative.binomial(theta = 0.0957), cocrdata) #conclude no rainfall effect
#
# aic.lmxnb(round(argcrdata$Tabanidae_bio*10), argcrdata)#m16 but model had troubles convergings, NaNs
# aic.lmxnb.init(round(argcrdata$Tabanidae_bio*10),569000, argcrdata)#m16 but theta too high
#
# aic.lmxnb(round(fulldata$TipulidaeLimoniidae_bio*10), fulldata)#m4 (site +k+k2) m2
# aic.lmxnb(round(noargcodata$Oligochaeta*1000), noargcodata)#m0 m1 (no rainfall effect)
# aic.lmxnb(round(camaprdata$Tanypodinae_bio*1000), camaprdata)#m1 m0 m3 (no rainfall effect)
# aic.lmxnb(round(cafgdata$predCerato_bio*1000), cafgdata)#m0 m1 (no rainfall effect)
# aic.lmxnb(round(cacrmadata$detCerato_bio*1000), cacrmadata) #m9 m0 m2 m1 (no rainfall effect)
# aic.lmxnb(round(nocodata$Culex*100), nocodata) #m7 (site*mu+mu2) m13
# aic.lmxnb(round(noargcacodata$Wyeomyia*100), noargcacodata)#m7 (site*mu+mu2)
# aic.lmxnb(round(nocodata$filtCulicidae*100), nocodata)#m7 (site*mu+mu2)
# aic.lmxnb(round(nofgdata$detChiron_bio*100), nofgdata)#m5 (site * mu)
#
# aic.sitenb(round(cadata$Calamoceratidae_bio*100), cadata)#m12 (mu+mu2)*(k+k2) believable? theta very high 357000
# aic.sitenb(round(fgdata$Cecidomyiidae_bio*100), fgdata) #m3 m0 m2 (no rainfall effect)
# aic.sitenb(round(prdata$Candonidae_bio*100), prdata)#m2 m4 m0 (no rainfall effect)
# aic.sitenb(round(prdata$Enchytraeoidae_bio*100), prdata) #m0 m1 (no rainfall effect)
# aic.sitenb(round(crdata$Hydrophilidae_bio*100), crdata) #m0 (no rainfall effect)
# aic.sitenb(round(crdata$Pseudostigmatidae_bio*100), crdata) #needs starting values but cant get even simple model to run
# aic.sitenb(round(cadata$Stratiomyidae_bio*100), cadata)#m9 (mu * k)
# aic.sitenb(round(cadata$Ephydridae_bio*100), cadata)#m9 mu * k) m10
# aic.sitenb(round(cadata$Dytiscidae_bio*10), cadata)#m3 m0 (no rainfall effect); but troubles with models
# aic.sitenb(round(cadata$Anopheles*100), cadata) #m3 (mu = mu2) m4
#
# aic.sitenb(round(cadata$Hirudinea*1000), cadata)#needs starting values, try Theta = 0.1606
# aic.site(round(cadata$Hirudinea*1000), family=negative.binomial(theta =0.1606), cadata)#nope
#
# b1<-glm.nb(Pseudostigmatidae_bio*10~log(maxvol), data = crdata); summary(b1)
#
# bestCoen<-glm.nb(round(Coenagrionidae_bio*10)~log(maxvol)+site+log(mu.scalar)*log(k.scalar), data = camadata)#only site sig
# residCoen<-resid(glm.nb(round(Coenagrionidae_bio*10)~log(maxvol)+site, data = camadata))
# aic.percent$draintrue[aic.percent$response=="Ceratopogonidae"]<-Dsquared(glm(residCoen~log(mu.scalar)*log(k.scalar),family=gaussian,camadata), adjust=TRUE)
#
# #sensitivity index engulfers
#
# fulldata$engulfer.sens.num<-
# (sumoutc$rain.percent[sumoutc$taxa=="Coenagrionidae_bio"]*fulldata$Coenagrionidae_bio+
# sumoutc$rain.percent[sumoutc$taxa=="Corethrellidae_bio"]*fulldata$Corethrellidae_bio+
# sumoutc$rain.percent[sumoutc$taxa=="Dytiscidae_bio"]*fulldata$Dytiscidae_bio+
# sumoutc$rain.percent[sumoutc$taxa=="Hirudinea"]*fulldata$Hirudinea+
# sumoutc$rain.percent[sumoutc$taxa=="Hydrophilidae_bio"]*fulldata$Hydrophilidae_bio+
# sumoutc$rain.percent[sumoutc$taxa=="Pseudostigmatidae_bio"]*fulldata$Pseudostigmatidae_bio)
# fulldata$engulfer.sens.denom<-
# (fulldata$Coenagrionidae_bio+ fulldata$Corethrellidae_bio+fulldata$Dytiscidae_bio+fulldata$Hirudinea+
# fulldata$Hydrophilidae_bio+fulldata$Pseudostigmatidae_bio)
# fulldata$engulfer.sens.index<-(fulldata$engulfer.sens.num/100)/(fulldata$engulfer.sens.denom)
# fulldata$engulfer.sens.index[fulldata$engulfer.sens.index == "NaN"] <- NA
# noargdata<-subset(fulldata,site!="argentina")
# noargcodata<-subset(noargdata,site!= "colombia")
# s1<-glm(log(engulfer.sens.denom)~engulfer.sens.index*site, data=noargcodata)
# par(mfrow=c(2,2)); plot(s1)
# Anova(s1, type=2) #super sig1
# visreg(s1, "engulfer.sens.index", by="site", scale="response") #most sites, as biomass is lost sensitivity goes down
#
# #sensitivity index gatherers
# fulldata$gatherer.sens.num<-
# (sumoutc$rain.percent[sumoutc$taxa=="detCerato_bio"]*fulldata$detCerato_bio+
# sumoutc$rain.percent[sumoutc$taxa=="detChiron_bio"]*fulldata$detChiron_bio+
# sumoutc$rain.percent[sumoutc$taxa=="Ephydridae_bio"]*fulldata$Ephydridae_bio+
# sumoutc$rain.percent[sumoutc$taxa=="Stratiomyidae_bio"]*fulldata$Stratiomyidae_bio+
# sumoutc$rain.percent[sumoutc$taxa=="Psychodidae_bio"]*fulldata$Psychodidae_bio+
# sumoutc$rain.percent[sumoutc$taxa=="Syrphidae_bio"]*fulldata$Syrphidae_bio+
# sumoutc$rain.percent[sumoutc$taxa=="Naididae_bio"]*fulldata$Oligochaeta)
# fulldata$gatherer.sens.denom<-
# (fulldata$detCerato_bio+fulldata$detChiron_bio+fulldata$Ephydridae_bio+
# fulldata$Stratiomyidae_bio+fulldata$Psychodidae_bio+fulldata$Syrphidae_bio+fulldata$Oligochaeta)
#
# fulldata$gatherer.sens.index<-(fulldata$gatherer.sens.num/100)/(fulldata$gatherer.sens.denom)
# fulldata$gatherer.sens.index[fulldata$gatherer.sens.index == "NaN"] <- NA
# s2<-glm(log(gatherer.sens.denom)~gatherer.sens.index*site, data=fulldata)
# par(mfrow=c(2,2)); plot(s2)
# Anova(s2, type=2) #super sig index, interaction marg
# visreg(s2, "gatherer.sens.index", by="site", scale="response") #most sites, as biomass is lost sensitivity goes UP!
#
# #sensitivity index piercers
# fulldata$piercer.sens.num<-
# (sumoutc$rain.percent[sumoutc$taxa=="Tanypodinae_bio"]*fulldata$Tanypodinae_bio+
# sumoutc$rain.percent[sumoutc$taxa=="Empididae_bio"]*fulldata$Empididae_bio+
# sumoutc$rain.percent[sumoutc$taxa=="Cecidomyiidae_bio"]*fulldata$Cecidomyiidae_bio+
# sumoutc$rain.percent[sumoutc$taxa=="Tabanidae_bio"]*fulldata$Tabanidae_bio+
# sumoutc$rain.percent[sumoutc$taxa=="predCerato_bio"]*fulldata$predCerato_bio)
# fulldata$piercer.sens.denom<-
# (fulldata$Tanypodinae_bio+fulldata$Empididae_bio+fulldata$Cecidomyiidae_bio+
# fulldata$Tabanidae_bio+fulldata$predCerato_bio)
# fulldata$piercer.sens.index<-(fulldata$piercer.sens.num/100)/(fulldata$piercer.sens.denom)
# fulldata$piercer.sens.index[fulldata$piercer.sens.index == "NaN"] <- NA
# nococrprdata<-subset(fulldata,site!="colombia")%>%subset(site!="costarica")%>%subset(site!="puertorico")
# s3<-glm(log(piercer.sens.denom)~piercer.sens.index*site, data=nococrprdata)
# par(mfrow=c(2,2)); plot(s3)
# Anova(s3, type=2) #super sig index, interaction marg
# visreg(s3, "piercer.sens.index", by="site", scale="response") #most sites, as biomass is lost sensitivity goes UP!
#
# #sensitivity index all
# fulldata$all.sens.num<-(fulldata$engulfer.sens.num+fulldata$gatherer.sens.num+fulldata$piercer.sens.num+
# sumoutc$rain.percent[sumoutc$taxa=="filtCulicidae"]*fulldata$filtCulicidae+
# sumoutc$rain.percent[sumoutc$taxa=="Candonidae_bio"]*fulldata$Candonidae_bio+
# sumoutc$rain.percent[sumoutc$taxa=="Scirtidae_bio"]*fulldata$Scirtidae_bio+
# sumoutc$rain.percent[sumoutc$taxa=="Limnocytheridae_bio"]*fulldata$Limnocytheridae_bio+
# sumoutc$rain.percent[sumoutc$taxa=="Calamoceratidae_bio"]*fulldata$Calamoceratidae_bio+
# sumoutc$rain.percent[sumoutc$taxa=="TipulidaeLimoniidae_bio"]*fulldata$TipulidaeLimoniidae_bio)
# fulldata$all.sens.denom<-(fulldata$engulfer.sens.denom+fulldata$gatherer.sens.denom+fulldata$piercer.sens.denom+
# fulldata$filtCulicidae+fulldata$Candonidae_bio+fulldata$Scirtidae_bio+fulldata$Limnocytheridae_bio+
# fulldata$Calamoceratidae_bio+fulldata$TipulidaeLimoniidae_bio)
# fulldata$all.sens.index<-(fulldata$all.sens.num/100)/(fulldata$all.sens.denom)
# fulldata$all.sens.index[fulldata$all.sens.index == "NaN"] <- NA
# s4<-glm(log(all.sens.denom)~all.sens.index*site, data=fulldata)
# par(mfrow=c(2,2)); plot(s4)
# Anova(s4, type=2) #super sig index, interaction marg
# visreg(s4, "all.sens.index", by="site", scale="response") #most sites, as biomass is lost sensitivity goes UP!
# allmodel(2,(fulldata$all.sens.index),poisson, fulldata)
## ===mantel tests====
#
# simmat.fncomp<-vegdist(sim, method="bray")
# pairsfc<-function(a,b,d){
# fngrp1<-fngrp
# fngrp1$site<-brom$site
# pairfn<-filter(fngrp1, site%in%c(a,b))%>%select(-site)
# pairx<-filter(brom, site%in%c(a,b))
# pair.dist<-vegdist(pairfn, method="jaccard")
# adon.comp<-adonis(pair.dist~log(mu.scalar)+log(k.scalar)+change.k+change.mu+maxvol+site+site:log(k.scalar)+site:log(mu.scalar)+log(mu.scalar):log(k.scalar)+site:log(mu.scalar):log(k.scalar)+site:change.k+site:change.mu+change.mu:change.k+site:change.mu:change.k, by="margin", pairx, perm = 1000)
# conting3<-sum(adon.comp$aov.tab$SumsOfSqs[c(7:8, 10:11, 13:14)])/adon.comp$aov.tab$SumsOfSqs[16]
# simmat.fncomp[d]<-conting3
# return(simmat.fncomp)
# }
#
# simmat.famcomp<-vegdist(sim, method="bray")
# pairsfam<-function(a,b,d){
# families1<-families
# families1$site<-brom$site
# pairfn<-filter(families1, site%in%c(a,b))%>%select(-site)
# pairx<-filter(brom, site%in%c(a,b))
# pair.dist<-vegdist(pairfn, method="jaccard")
# adon.comp<-adonis(pair.dist~log(mu.scalar)+log(k.scalar)+change.k+change.mu+maxvol+site+site:log(k.scalar)+site:log(mu.scalar)+log(mu.scalar):log(k.scalar)+site:log(mu.scalar):log(k.scalar)+site:change.k+site:change.mu+change.mu:change.k+site:change.mu:change.k, by="margin", pairx, perm = 1000)
# conting3<-sum(adon.comp$aov.tab$SumsOfSqs[c(7:8, 10:11, 13:14)])/adon.comp$aov.tab$SumsOfSqs[16]
# simmat.famcomp[d]<-conting3
# return(simmat.famcomp)
# }
#
#
# pairs1<-function(b,c,d){
# a<-rbind(b,c)
# n<-1
# sum.outq<-data.frame(full.d2=numeric(n),noint.d2=numeric(n), intend.d2=numeric(n), site.d2=numeric(n), conting.p=numeric(n),general.p=numeric(n), siterain.p=numeric(n), rainfall.p=numeric(n), sensitive.p=numeric(n))
# sum.outq[1,]<-allmodel(2,a$gatherer_bio,poisson, a)
# conting<-(sum.outq[,1]-sum.outq[,2])
# simmatg[d]<-conting
# return(simmatg)
# }#gatherer pairwise site effect
#
# pairs2<-function(b,c,d){
# a<-rbind(b,c)
# n<-1
# sum.outq<-data.frame(full.d2=numeric(n),noint.d2=numeric(n), intend.d2=numeric(n), site.d2=numeric(n), conting.p=numeric(n),general.p=numeric(n), siterain.p=numeric(n), rainfall.p=numeric(n),sensitive.p=numeric(n))
# sum.outq[1,]<-allmodel(2,a$engulfer_bio,poisson, a)
# print(sum.outq)
# conting<-(sum.outq[,1]-sum.outq[,2])
# simmate[d]<-conting
# return(simmate)
# }#engulfer pairwise site effect
#
# pairs3<-function(b,c,d){
# a<-rbind(b,c)
# n<-1
# sum.outf<-data.frame(full.d2=numeric(n),noint.d2=numeric(n), intend.d2=numeric(n), site.d2=numeric(n), conting.p=numeric(n),general.p=numeric(n), siterain.p=numeric(n), rainfall.p=numeric(n),sensitive.p=numeric(n))
# sum.outf[1,]<-allmodel(2,a$filter.feeder_bio,poisson, a)
# print(sum.outf)
# conting<-(sum.outf[,1]-sum.outf[,2])
# simmatf[d]<-conting
# return(simmatf)
# }#filterer pairwise site effect
#
#
#
# # ardata cadata codata crdata fgdata madata
# #cadata 1
# #codata 2 7
# #crdata 3 8 12
# #fgdata 4 9 13 16
# #madata 5 10 14 17 19
# #prdata 6 11 15 18 20 21
#
# simmat.fncomp<-pairsfc("cardoso", "argentina",1);simmat.fncomp<-pairsfc("colombia", "argentina",2);simmat.fncomp<-pairsfc("costarica", "argentina",3)
# simmat.fncomp<-pairsfc("frenchguiana", "argentina",4);simmat.fncomp<-pairsfc("macae", "argentina",5);simmat.fncomp<-pairsfc("puertorico", "argentina",6)
# simmat.fncomp<-pairsfc("colombia", "cardoso",7);simmat.fncomp<-pairsfc("costarica", "cardoso",8);simmat.fncomp<-pairsfc("frenchguiana", "cardoso",9)
# simmat.fncomp<-pairsfc("macae", "cardoso",10);simmat.fncomp<-pairsfc("puertorico", "cardoso",11);simmat.fncomp<-pairsfc("costarica", "colombia",12)
# simmat.fncomp<-pairsfc("frenchguiana", "colombia",13);simmat.fncomp<-pairsfc("macae", "colombia",14);simmat.fncomp<-pairsfc("puertorico", "colombia",15)
# simmat.fncomp<-pairsfc("frenchguiana", "costarica",16);simmat.fncomp<-pairsfc("macae", "costarica",17);simmat.fncomp<-pairsfc("puertorico", "costarica",18)
# simmat.fncomp<-pairsfc("macae", "frenchguiana",19);simmat.fncomp<-pairsfc("puertorico", "frenchguiana",20);simmat.fncomp<-pairsfc("puertorico", "macae",21)
# simmat.fncomp
#
# simmat.famcomp<-pairsfam("cardoso", "argentina",1);simmat.famcomp<-pairsfam("colombia", "argentina",2);simmat.famcomp<-pairsfam("costarica", "argentina",3)
# simmat.famcomp<-pairsfam("frenchguiana", "argentina",4);simmat.famcomp<-pairsfam("macae", "argentina",5);simmat.famcomp<-pairsfam("puertorico", "argentina",6)
# simmat.famcomp<-pairsfam("colombia", "cardoso",7);simmat.famcomp<-pairsfam("costarica", "cardoso",8);simmat.famcomp<-pairsfam("frenchguiana", "cardoso",9)
# simmat.famcomp<-pairsfam("macae", "cardoso",10);simmat.famcomp<-pairsfam("puertorico", "cardoso",11);simmat.famcomp<-pairsfam("costarica", "colombia",12)
# simmat.famcomp<-pairsfam("frenchguiana", "colombia",13);simmat.famcomp<-pairsfam("macae", "colombia",14);simmat.famcomp<-pairsfam("puertorico", "colombia",15)
# simmat.famcomp<-pairsfam("frenchguiana", "costarica",16);simmat.famcomp<-pairsfam("macae", "costarica",17);simmat.famcomp<-pairsfam("puertorico", "costarica",18)
# simmat.famcomp<-pairsfam("macae", "frenchguiana",19);simmat.famcomp<-pairsfam("puertorico", "frenchguiana",20);simmat.famcomp<-pairsfam("puertorico", "macae",21)
# simmat.famcomp
#
#
# simmatg<-vegdist(sim, method="bray")
# simmatg<-pairs1(cadata, ardata,1);simmatg<-pairs1(codata, ardata,2);simmatg<-pairs1(crdata, ardata,3)
# simmatg<-pairs1(fgdata, ardata,4);simmatg<-pairs1(madata, ardata,5);simmatg<-pairs1(prdata, ardata,6)
# simmatg<-pairs1(codata, cadata,7);simmatg<-pairs1(crdata, cadata,8);simmatg<-pairs1(fgdata, cadata,9)
# simmatg<-pairs1(madata, cadata,10);simmatg<-pairs1(prdata, cadata,11);simmatg<-pairs1(crdata, codata,12)
# simmatg<-pairs1(fgdata, codata,13);simmatg<-pairs1(madata, codata,14);simmatg<-pairs1(prdata, codata,15)
# simmatg<-pairs1(fgdata, crdata,16);simmatg<-pairs1(madata, crdata,17);simmatg<-pairs1(prdata, crdata,18)
# simmatg<-pairs1(madata, fgdata,19);simmatg<-pairs1(prdata, fgdata,20);simmatg<-pairs1(prdata, madata,21)
# simmatg
#
# #engulfers: no arg, co, pr
#
# # ca cr fg
# #crdata 1
# #fgdata 2 4
# #madata 3 5 6
#
# sim2<-as.data.frame(rep(1,4))
# simmate<-vegdist(sim2, method="bray")
# simmate<-pairs2(crdata, cadata,1);simmate<-pairs2(fgdata, cadata,2);simmate<-pairs2(madata, cadata,3)
# simmate<-pairs2(fgdata, crdata,4);simmate<-pairs2(madata, crdata,5);simmate<-pairs2(fgdata, madata,6)
# simmate
#
# pool<-cbind(select(fulldata, site), families)
# sppool<-group_by(pool, site)%>%
# summarise(
# a=sum(Calamoceratidae_bio),
# b=sum(Candonidae_bio),
# c=sum(Cecidomyiidae_bio),
# d=sum(Ceratopogonidae_bio),
# e=sum(Chironomidae_bio),
# f=sum(Coenagrionidae_bio),
# g=sum(Corethrellidae_bio),
# h=sum(Culicidae_bio),
# i=sum(Dytiscidae_bio),
# j=sum(Empididae_bio),
# k=sum(Enchytraeoidae_bio),
# l=sum(Ephydridae_bio),
# m=sum(Hydrophilidae_bio),
# n=sum(Lampyridae_bio),
# o=sum(Limnocytheridae_bio),
# p=sum(Naididae_bio),
# q=sum(Pseudostigmatidae_bio),
# r=sum(Psychodidae_bio),
# s=sum(Ptilodactylidae_bio),
# t=sum(Scirtidae_bio),
# u=sum(Stratiomyidae_bio),
# v=sum(Syrphidae_bio),
# w=sum(Tabanidae_bio),
# x=sum(TipulidaeLimoniidae_bio),
# y=sum(Hirudinea),
# z=sum(Dolichopodidae_bio))
# sppool2<-select(sppool, -site)
# row.names(sppool2)<-sppool$site
# poolhell<-decostand(sppool2,method="hellinger")
# simpool<-vegdist(poolhell, method="jaccard")
# simpool
#
# mantel(simmat.fncomp, simpool, method="pearson", permutations=100) #not sig r=-0.332, p =0.87
# mantel(simmat.fncomp, simmat.famcomp, method="pearson", permutations=1000) #r = 0.78, p = 0.002
#
#
# sppool.gatherer<-select(sppool2,d,e,l,u,r,v,p,k)
# row.names(sppool.gatherer)<-sppool$site
# poolhell.gath<-decostand(sppool.gatherer,method="hellinger")
# simpool.gath<-vegdist(poolhell.gath, method="bray")
# simpool.gath
#
# mantel(simmatg, simpool.gath, method="pearson", permutations=5039) #not sig just for gatherers r = -0.41, p=0.92
#
# simmatg3<-vegdist(sim, method="bray")
# pairsg3<-function(a,b,d){
# gath_bio$site<-brom$site
# gath_bio_nozero<-filter(gath_bio,gath_sum>0)%>%filter(site%in%c(a,b))%>%select(-gath_sum, -site)
# xvar.gath<-filter(brom,gath_sum>0)%>%filter(site%in%c(a,b))
# gath.dist<-vegdist(gath_bio_nozero)
# adon.gath<-adonis(gath.dist~log(mu.scalar)+log(k.scalar)+change.k+change.mu+maxvol+site+site:log(k.scalar)+site:log(mu.scalar)+log(mu.scalar):log(k.scalar)+site:log(mu.scalar):log(k.scalar)+site:change.k+site:change.mu+change.mu:change.k+site:change.mu:change.k, by="margin", xvar.gath, perm = 1000)
# conting3<-sum(adon.gath$aov.tab$SumsOfSqs[c(7:8, 10:11, 13:14)])/adon.gath$aov.tab$SumsOfSqs[16]
# simmatg3[d]<-conting3
# return(simmatg3)
# }
#
# simmatg3<-pairsg3("cardoso", "argentina",1);simmatg3<-pairsg3("colombia", "argentina",2);simmatg3<-pairsg3("costarica", "argentina",3)
# simmatg3<-pairsg3("frenchguiana", "argentina",4);simmatg3<-pairsg3("macae", "argentina",5);simmatg3<-pairsg3("puertorico", "argentina",6)
# simmatg3<-pairsg3("colombia", "cardoso",7);simmatg3<-pairsg3("costarica", "cardoso",8);simmatg3<-pairsg3("frenchguiana", "cardoso",9)
# simmatg3<-pairsg3("macae", "cardoso",10);simmatg3<-pairsg3("puertorico", "cardoso",11);simmatg3<-pairsg3("costarica", "colombia",12)
# simmatg3<-pairsg3("frenchguiana", "colombia",13);simmatg3<-pairsg3("macae", "colombia",14);simmatg3<-pairsg3("puertorico", "colombia",15)
# simmatg3<-pairsg3("frenchguiana", "costarica",16);simmatg3<-pairsg3("macae", "costarica",17);simmatg3<-pairsg3("puertorico", "costarica",18)
# simmatg3<-pairsg3("macae", "frenchguiana",19);simmatg3<-pairsg3("puertorico", "frenchguiana",20);simmatg3<-pairsg3("puertorico", "macae",21)
# simmatg3
#
# mantel(simmatg, simmatg3, method="pearson", permutations=999) #yeah! r=0.515, p = 0.037
# mantel(simpool.gath, simmatg3, method="pearson", permutations=999) #taxonomic conting unrelated to similarity in pool
# mantel.partial(simmatg, simmatg3, simpool.gath, method="pearson", permutations=999)#still sig after pulling out taxonomic diff in species pools
# mantel.partial(simpool.gath, simmatg3, simmatg, method="pearson", permutations=999)
#
# exclude<-c("argentina", "colombia", "puertorico")
# sppool.engulfer<-select(sppool,site,f,g,i,m,q,y,z)%>%filter(site%nin%exclude)%>%select(-site)
# poolhell.eng<-decostand(sppool.engulfer,method="hellinger")
# simpool.eng<-vegdist(poolhell.eng, method="bray")
# simpool.eng
# mantel(simmate, simpool.eng, method="pearson", permutations=999) #r = 0.442, p=0.33
#
# sim2<-as.data.frame(rep(1,4))
# simmate3<-vegdist(sim2, method="bray")
#
# pairse3<-function(a,b,d){
# eng_bio$site<-brom$site
# eng_bio_nozero<-filter(eng_bio,eng_sum>0)%>%filter(site%in%c(a,b))%>%select(-eng_sum, -site)
# xvar.eng<-filter(brom,eng_sum>0)%>%filter(site%in%c(a,b))
# eng.dist<-vegdist(eng_bio_nozero)
# adon.eng<-adonis(eng.dist~log(mu.scalar)+log(k.scalar)+change.k+change.mu+maxvol+site+site:log(k.scalar)+site:log(mu.scalar)+log(mu.scalar):log(k.scalar)+site:log(mu.scalar):log(k.scalar)+site:change.k+site:change.mu+change.mu:change.k+site:change.mu:change.k, by="margin", xvar.eng, perm = 1000)
# conting3<-sum(adon.eng$aov.tab$SumsOfSqs[c(7:8, 10:11, 13:14)])/adon.eng$aov.tab$SumsOfSqs[16]
# simmate3[d]<-conting3
# return(simmate3)
# }
#
# simmate3<-pairse3("costarica", "cardoso",1);simmate3<-pairse3("frenchguiana", "cardoso",2);simmate3<-pairse3("macae", "cardoso",3)
# simmate3<-pairse3("frenchguiana", "costarica",4);simmate3<-pairse3("macae", "costarica",5);simmate3<-pairse3("frenchguiana", "macae",6)
# simmate3
#
# mantel(simmate, simmate3, method="pearson", permutations=23) #r=0.858, p =0.12 looks like an effect, but not enouh power
#
#
# #filter(invert_traits, site=="puertorico")%>%filter(functional_group=="engulfer")%>%View #all playhelminthes
# filter(invert_traits, site=="argentina")%>%filter(functional_group=="engulfer")%>%View
# filter(invert_traits, site=="costarica")%>%filter(functional_group=="engulfer")%>%View
# filter(invert_traits, site=="macae")%>%filter(functional_group=="engulfer")%>%View
# filter(invert_traits, site=="cardoso")%>%filter(functional_group=="engulfer")%>%View
# filter(invert_traits, site=="frenchguiana")%>%filter(functional_group=="engulfer")%>%View
# filter(invert_traits, site=="colombia")%>%filter(functional_group=="engulfer")%>%View
#
# simdist<-vegdist(sim, method="euclidean")
# simdist[1]<-1336;simdist[2]<-4078; simdist[3]<-5234; simdist[4]<-3920; simdist[5]<-2027; simdist[6]<-5342
# simdist[7]<-4365;simdist[8]<-5711; simdist[9]<-3395; simdist[10]<-691; simdist[11]<-5195; simdist[12]<-1420
# simdist[13]<-2340;simdist[14]<-4641; simdist[15]<-1759; simdist[16]<-3624; simdist[17]<-6035; simdist[18]<-2263
# simdist[19]<-3290;simdist[20]<-2020; simdist[21]<-5224
# simdist
#
# mantel(simmat.fncomp, simdist, method="pearson", permutations=5000)
# mantel(simmat, simdist, method="pearson", permutations=5000) #not sig
# mantel(simmat, simpool, method="pearson", permutations=5000) #not sig
# mantel.partial(simmat, simpool, simdist, method="pearson", permutations=5000) #not sig
#
# #hydrologic sensitivity
#
#
# pairsh<-function(a,b,d){
# hydro1<-purehydro
# hydro1$site<-brom.noca$site
# pairhydro<-filter(hydro1, site%in%c(a,b))%>%select(-site)
# pairx<-filter(brom.noca, site%in%c(a,b))
# pair.h.dist<-vegdist(pairhydro, method="jaccard")
# adon.comp<-adonis(pair.h.dist~log(mu.scalar)+log(k.scalar)+change.k+change.mu+maxvol+site+site:log(k.scalar)+site:log(mu.scalar)+log(mu.scalar):log(k.scalar)+site:log(mu.scalar):log(k.scalar)+site:change.k+site:change.mu+change.mu:change.k+site:change.mu:change.k, by="margin", pairx, perm = 1000)
# conting3<-sum(adon.comp$aov.tab$SumsOfSqs[c(7:8, 10:11, 13:14)])/adon.comp$aov.tab$SumsOfSqs[16]
# simmath[d]<-conting3
# return(simmath)
# }
#
# simmat.h.fncomp<-vegdist(sim3, method="bray")
# pairshfc<-function(a,b,d){
# fngrp1<-fngrp
# fngrp1$site<-brom$site
# pairfn<-filter(fngrp1, site%in%c(a,b))%>%select(-site)
# pairx<-filter(brom, site%in%c(a,b))
# pair.dist<-vegdist(pairfn, method="jaccard")
# adon.comp<-adonis(pair.dist~log(mu.scalar)+log(k.scalar)+change.k+change.mu+maxvol+site+site:log(k.scalar)+site:log(mu.scalar)+log(mu.scalar):log(k.scalar)+site:log(mu.scalar):log(k.scalar)+site:change.k+site:change.mu+change.mu:change.k+site:change.mu:change.k, by="margin", pairx, perm = 1000)
# conting3<-sum(adon.comp$aov.tab$SumsOfSqs[c(7:8, 10:11, 13:14)])/adon.comp$aov.tab$SumsOfSqs[16]
# simmat.h.fncomp[d]<-conting3
# return(simmat.h.fncomp)
# }
#
# # ardata codata crdata fgdata madata
# #codata 1
# #crdata 2 6
# #fgdata 3 7 10
# #madata 4 8 11 13
# #prdata 5 9 12 14 15
#
# sim3<-subset(sim,rownames(sim)!="cadata")
# simmath<-vegdist(sim3, method="bray")
# simmath<-pairsh("colombia", "argentina",1);simmath<-pairsh("costarica", "argentina",2);simmath<-pairsh("frenchguiana", "argentina",3)
# simmath<-pairsh("macae", "argentina",4);simmath<-pairsh("puertorico", "argentina",5);simmath<-pairsh("colombia", "costarica",6)
# simmath<-pairsh("colombia", "frenchguiana",7);simmath<-pairsh("macae", "colombia",8);simmath<-pairsh("puertorico", "colombia",9)
# simmath<-pairsh("frenchguiana", "costarica",10);simmath<-pairsh("macae", "costarica",11);simmath<-pairsh("costarica", "puertorico",12)
# simmath<-pairsh("frenchguiana", "macae",13);simmath<-pairsh("frenchguiana", "puertorico",14);simmath<-pairsh("puertorico","macae",15)
# simmath
#
# simmat.h.fncomp<-vegdist(sim3, method="bray")
# simmat.h.fncomp<-pairshfc("colombia", "argentina",1);simmat.h.fncomp<-pairshfc("costarica", "argentina",2);simmat.h.fncomp<-pairshfc("frenchguiana", "argentina",3)
# simmat.h.fncomp<-pairshfc("macae", "argentina",4);simmat.h.fncomp<-pairshfc("puertorico", "argentina",5);simmat.h.fncomp<-pairshfc("colombia", "costarica",6)
# simmat.h.fncomp<-pairshfc("colombia", "frenchguiana",7);simmat.h.fncomp<-pairshfc("macae", "colombia",8);simmat.h.fncomp<-pairshfc("puertorico", "colombia",9)
# simmat.h.fncomp<-pairshfc("frenchguiana", "costarica",10);simmat.h.fncomp<-pairshfc("macae", "costarica",11);simmat.h.fncomp<-pairshfc("costarica", "puertorico",12)
# simmat.h.fncomp<-pairshfc("frenchguiana", "macae",13);simmat.h.fncomp<-pairshfc("frenchguiana", "puertorico",14);simmat.h.fncomp<-pairshfc("puertorico","macae",15)
# simmat.h.fncomp
#
# mantel(simmath, simmat.h.fncomp, method="pearson", permutations=1000) # r=0.55, p = 0.036 (worth pursuing!)jaccard+jaccard
#
# simmatf<-vegdist(sim3, method="bray")
# simmatf<-pairs3(codata, ardata,1);simmatf<-pairs3(crdata, ardata,2);simmatf<-pairs3(fgdata, ardata,3)
# simmatf<-pairs3(madata, ardata,4);simmatf<-pairs3(prdata, ardata,5);simmatf<-pairs3(codata, crdata,6)
# simmatf<-pairs3(codata, fgdata,7);simmatf<-pairs3(madata, codata,8);simmatf<-pairs3(prdata, codata,9)
# simmatf<-pairs3(fgdata, crdata,10);simmatf<-pairs3(madata, crdata,11);simmatf<-pairs3(crdata, prdata,12)
# simmatf<-pairs3(fgdata, madata,13);simmatf<-pairs3(fgdata, prdata,14);simmatf<-pairs3(prdata, madata,15)
# simmatf
#
# mantel(simmath, simmatf, method="pearson", permutations=5000)#blast r=0.28, p =0.16 even filter feeder contin not related to propdried days conting
# #also tried for gatherers, nonsig
#
#
# #species interactions
#
# b1<-glm(scraper_bio~maxvol+engulfer_bio, data =madata, family = poisson)
# summary(b1)
# Anova(b1, type=2) #engulfer_bio sig and negative effect, but looks like its drien by two bromeliads
# visreg(b1, "detritivore_bio")
#
# b1<-glm(shredder_bio~maxvol+predator_bio, data =fgdata, family = poisson)#sig
# b1<-glm(detritivore_bio~maxvol+predator_bio, data =fgdata, family = poisson)#marg
#
# b1<-glm(detritivore_bio~maxvol+predator_bio, data =ardata, family = poisson) #sig
#
# b1<-glm(shredder_bio~maxvol+predator_bio, data =crdata, family = poisson)#this is just sig, plot looks convincing
#
# b1<-glm(detritivore_bio~maxvol+Odonata_bio, data =madata, family = poisson) #just sig
#
# b3<-glm(detritivore_bio~maxvol+predator_bio*site, data =nocacoprdata, family = poisson)
# padj<-b3$deviance/b3$df.residual
# b4<-glm(detritivore_bio/padj~maxvol+predator_bio*site, data =nocoprdata, family = poisson)
# Anova(b4, type=2)
# b5<-glm.nb(round(detritivore_bio*10)~maxvol+predator_bio*site, data =nocacoprdata) #fg is watering down
# b6<-glm.nb(round(detritivore_bio*10)~maxvol+predator_bio*site, data =filter(fulldata,site%in%c("costarica", "macae", "cardoso"))) #sig general effect
# #note that these 3 sites have lots of odonates, but odonate biomass not the only driver (see below)
# b6<-glm.nb(round(detritivore_bio*10)~maxvol+predator_bio*site, data =filter(fulldata,site%nin%c("puertorico")))
# tapply(fulldata$Odonata_bio, fulldata$site, datacheck)
# b6<-glm.nb(round(detritivore_bio*10)~maxvol+Odonata_bio*site, data =filter(fulldata,site%in%c("costarica", "macae", "cardoso"))) #no
#
# b2<-rq((shredder_bio)^0.5~maxvol+predator_bio, tau = 0.05, data =madata)
# summary(b2, se = "boot")
# fullplot(crdata$predator_bio, poisson, crdata)
# fullsum(cacrmadata$Odonata_bio, poisson, cacrmadata)
# m25a<-glm(predator_bio~maxvol+(mu.scalar+I(mu.scalar^2))*(k.scalar+I(k.scalar^2)), family=poisson, data=madata);
# padj<-m25a$deviance/m25a$df.residual
# m25b<-glm(predator_bio/padj~maxvol+(mu.scalar+I(mu.scalar^2))*(k.scalar+I(k.scalar^2)), family=poisson, data=madata);
# plot((cadata$detritivore_bio)^0.5~(cadata$predator_bio))
# Anova(m25b, type=2, test="LR")
#
# m25a<-glm(detritivore_bio~maxvol+(mu.scalar+I(mu.scalar^2))*(k.scalar+I(k.scalar^2)), family=poisson, data=ardata);
# padj<-m25a$deviance/m25a$df.residual
# m25b<-glm(detritivore_bio/padj~maxvol+(mu.scalar+I(mu.scalar^2))*(k.scalar+I(k.scalar^2)), family=poisson, data=ardata)
# m25c<-glm(detritivore_bio/padj~maxvol, family=poisson, data=ardata)
# anova(m25b, m25c, test= "LRT") # rainfall, largely k, explains 18.5 deviance
# Anova(m25b, type = 2)
# m25d<-glm(detritivore_bio/padj~maxvol+predator_bio+(mu.scalar+I(mu.scalar^2))*(k.scalar+I(k.scalar^2)), family=poisson, data=ardata)
# m25e<-glm(detritivore_bio/padj~maxvol+predator_bio, family=poisson, data=ardata)
# anova(m25d, m25e, test= "LRT")#rainfall explains 20.2 deviance??
# anova(m25d, test= "LRT")#predator no longer sig!
#
# #====correlation tests
# #==note filter low in co, cr; engul in arg, co, piercer in co, cr,pr===
#
# corr.fngrps<-function (dataset)
# {out<-1:15
# r.shredder<-resid(glm(shredder_bio~log(maxvol),family=poisson, data=dataset))
# r.filter.feeder<-resid(glm(filter.feeder_bio~log(maxvol),family=poisson, data=dataset))
# r.scraper<-resid(glm(scraper_bio~log(maxvol),family=poisson, data=dataset))
# r.gatherer<-resid(glm(gatherer_bio~log(maxvol),family=poisson, data=dataset))
# r.engulfer<-resid(glm(engulfer_bio~log(maxvol),family=poisson, data=dataset))
# r.piercer<-resid(glm(piercer_bio~log(maxvol), family=poisson, data=dataset))
# out[1]<-cor.test(r.shredder,r.filter.feeder, na.rm=TRUE)$estimate
# out[2]<-cor.test(r.shredder,r.scraper, na.rm=TRUE)$estimate
# out[3]<-cor.test(r.scraper,r.filter.feeder, na.rm=TRUE)$estimate
# out[4]<-cor.test(r.shredder,r.gatherer, na.rm=TRUE)$estimate
# out[5]<-cor.test(r.filter.feeder,r.gatherer, na.rm=TRUE)$estimate
# out[6]<-cor.test(r.scraper,r.gatherer, na.rm=TRUE)$estimate
# out[7]<-cor.test(r.engulfer,r.shredder, na.rm=TRUE)$estimate
# out[8]<-cor.test(r.engulfer,r.filter.feeder, na.rm=TRUE)$estimate
# out[9]<-cor.test(r.engulfer,r.scraper, na.rm=TRUE)$estimate
# out[10]<-cor.test(r.engulfer,r.gatherer, na.rm=TRUE)$estimate
# out[11]<-cor.test(r.piercer,r.shredder, na.rm=TRUE)$estimate
# out[12]<-cor.test(r.piercer,r.filter.feeder, na.rm=TRUE)$estimate
# out[13]<-cor.test(r.piercer,r.scraper, na.rm=TRUE)$estimate
# out[14]<-cor.test(r.piercer,r.gatherer, na.rm=TRUE)$estimate
# out[15]<-cor.test(r.piercer,r.engulfer, na.rm=TRUE)$estimate
# return(out)
# }
#
#
# site<-c(rep("macae",15),rep("costarica", 15),rep("cardoso",15), rep("frenchguiana",15), rep("puertorico",15), rep("argentina",15), rep("colombia",15))
# corrfg<-as.data.frame(site)
# corrfg$fngrp1<-rep(c("filter.feeder", "scraper", "scraper", "gatherer", "gatherer", "gatherer", rep("engulfer",4), rep("piercer",5)),7)
# corrfg$fngrp2<-rep(c("shredder", "shredder", "filter.feeder", "shredder", "filter.feeder", "scraper","shredder", "filter.feeder", "scraper", "gatherer","shredder", "filter.feeder", "scraper", "gatherer", "engulfer"),7)
# corrfg$trophic<-rep(c(rep("yes",6), rep("no",8), rep("no",1)),7)
# corrfg$corr<-1:105
# corrfg$corr[1:15]<-corr.fngrps(madata)
# corrfg$corr[16:30]<-corr.fngrps(crdata)
# corrfg$corr[31:45]<-corr.fngrps(cadata)
# corrfg$corr[46:60]<-corr.fngrps(fgdata)
# corrfg$corr[61:75]<-corr.fngrps(prdata)
# corrfg$corr[76:90]<-corr.fngrps(ardata)
# corrfg$corr[91:105]<-corr.fngrps(codata)
#
# mean(corrfg$corr)#r = 0.029
# 1.96*sd(corrfg$corr)/(105^0.5) # =/- 0.037
# mean(corrfg$corr[corrfg$trophic=="yes"&corrfg$site!="puertorico"])#r=0.059, or 0.074 without PR
# 1.96*sd(corrfg$corr[corrfg$trophic=="yes"])
# mean(corrfg$corr[corrfg$trophic=="no"&corrfg$site!="puertorico"])#r=0.00267, or -0.02 without PR
# 1.96*sd(corrfg$corr[corrfg$trophic=="no"])
# anova(lm(corr~trophic, data=corrfg)) #p=0.1375
# Anova(lm(corr~trophic*site, data=corrfg), type=2) #p=0.039, diff overall between fn grps in same vs. diff trophic level
# Anova(lm(corr~trophic*site, data=subset(corrfg, site!="puertorico")), type=2)#p=0.02668
#
# pval<-rep(1,1000)
# for (i in 1:1000) {
# corrfg$ptrophic<-sample(corrfg$trophic, replace=FALSE)
# pval[i]<-Anova(lm(corr~ptrophic*site, data=subset(corrfg, site!="puertorico")), type=2)$Pr[1]#more sig
# }
# sort(pval)[25]#0.029...so our pvalue is just sig, but wouldn't be without excl. PR..remove flatworms?
# sort(pval)[975]#0.978
#
# subset(corrfg, site!="puertorico")
# mean(corr.fngrps(madata)[1:6])#0.068
# 1.96*(sd(corr.fngrps(madata)[1:6]))/(6^0.5)#0.2
# mean(corr.fngrps(madata)[7:14])#-0.09
# 1.96*(sd(corr.fngrps(madata)[7:14]))/(8^0.5)# 0.11
#
# mean(corr.fngrps(crdata)[1:6])#0.12
# 1.96*(sd(corr.fngrps(crdata)[1:6]))/(6^0.5)#0.18
# mean(corr.fngrps(crdata)[7:14])#-0.02
# 1.96*(sd(corr.fngrps(crdata)[7:14]))/(8^0.5)# 0.13
#
# mean(corr.fngrps(fgdata)[1:6])#0.04
# 1.96*(sd(corr.fngrps(fgdata)[1:6]))/(6^0.5)#0.09
# mean(corr.fngrps(fgdata)[7:14])#-0.09
# 1.96*(sd(corr.fngrps(fgdata)[7:14]))/(8^0.5)# 0.08
#
# mean(corr.fngrps(prdata)[1:6])#0.09
# 1.96*(sd(corr.fngrps(prdata)[1:6]))/(6^0.5)#0.136
# mean(corr.fngrps(prdata)[7:14])#0.10
# 1.96*(sd(corr.fngrps(prdata)[7:14]))/(8^0.5)# 0.178
#
# #================Co2, decomp and heterotrophs
#
# #sqrt(fulldata$decomp),gaussian, (no126data$n15.bromeliad.final+4)^0.125, log(nocodata$co2.final),gaussian, nocodata); rowName[9]<-"CO2", noargco123data$bacteria.per.ml.final/1000000
#
# sm1<-lmer(log(co2.final)~log(maxvol)+bacteria.per.nl.final+decomp+totalbio+ (1| site), data=nocaprdata)
# par(mfrow=c(2,2)); plot(sm1)
# Anova(sm1,type = 2) # logmaxvol and decomp sig without site random, nthing with random effect site (slope or intercept)
# anova(sm1)
# sm1b<-lmer(log(co2.final)~log(maxvol)+decomp+ (1| site), data=nocaprdata); Anova(sm1b,type = 2)
# anova(sm1b)
# sm1b<-lmer(log(co2.final)~log(maxvol)+bacteria.per.nl.final+ (bacteria.per.nl.final| site), data=nocaprdata); Anova(sm1b,type = 2)
# anova(sm1b)
# sm1c<-glm(log(co2.final)~log(maxvol)+bacteria.per.nl.final*site+totalbio*site, data=nocacoprdata, family = gaussian)
# Anova(sm1c,type = 2) #both site and bacteria * site sig
# visreg(sm1c, "bacteria.per.nl.final", by = "site", ylab="CO2 flux")
# sm1b<-lmer(log(co2.final)~log(maxvol)+cv_mean_temp+(1| site), data=nocaprdata); Anova(sm1b,type = 2)
# anova(sm1b)#aha, finally! even within sites, cv_mean_temp important for CO2
# visreg(sm1b, "cv_mean_temp", by= "site",ylab="CO2 flux")
# summary(sm1b) #increasing cv of mean temp reduces CO2
#
# sm2<-lmer((decomp)^0.5~log(maxvol)+bacteria.per.nl.final+totalbio+ (1| site), data=noargco123data)
# par(mfrow=c(2,2)); plot(sm2)
# Anova(sm2,type = 2) # here bacteria (marg) and total bio sig without random site; nothing sig with random site
# sm2b<-lmer((decomp)^0.5~log(maxvol)+bacteria.per.nl.final+detritivore_bio+ (1| site), data=noargco123data); Anova(sm2b,type = 2)
# summary(sm2b)#now log max vol, detritivore biomass and bacteria density sg without random effect, not with...
# anova(sm2b)
# sm3b<-glm((decomp)^0.5~log(maxvol)+bacteria.per.nl.final*site, data=noargco123data, family = gaussian); Anova(sm3b,type = 2)
# summary(sm3b) # nothing but site sig
# Anova(sm3b, type=2)
# visreg(sm3b, "bacteria.per.nl.final", by ="site", ylab="decomp")
# sm2b<-lmer((decomp)^0.5~log(maxvol)+cv_mean_temp+mean_temp+ (1| site), data=noargco123datatemp); Anova(sm2b,type = 2)
#
# sm1b<-lmer(log(bacteria.per.nl.final)~log(maxvol)+mean_temp+(mean_temp| site), data=nocaprdata); Anova(sm1b,type = 2)
# anova(sm1b)#aha, finally! even within sites, mean_temp positively related to bacteria (esp macae)
# visreg(sm1b, "mean_temp", by = "site",type = "contrast", ylab="bacteria")
# sm1b<-glm(log(bacteria.per.nl.final)~log(maxvol)+mean_temp*site, data=nocaprdata); Anova(sm1b,type = 2)
#
# tapply(fulldata$bacteria.per.nl.final, fulldata$site, datacheck)#all but arg
# tapply(fulldata$co2.final, fulldata$site, datacheck)#all but ca, pr
#
# mfull<-glm(bacteria.per.nl.final~log(maxvol)+site*(mu.scalar+I(mu.scalar^2))*(k.scalar+I(k.scalar^2)), family=poisson, data=noargco123datatemp)
# padj<-mfull$deviance/mfull$df.residual
# mfull<-glm((bacteria.per.nl.final)/padj~log(maxvol)+site*(mu.scalar+I(mu.scalar^2))*(k.scalar+I(k.scalar^2)), family=poisson, data=noargco123datatemp)
# Anova(mfull, type=2)
# mfull.noint<-glm((bacteria.per.nl.final)/padj~log(maxvol)+site+(mu.scalar+I(mu.scalar^2))*(k.scalar+I(k.scalar^2)), family=poisson, data=noargco123datatemp)
# anova(mfull, mfull.noint, test="Chisq")#sig conting
# mfulla<-glm((bacteria.per.nl.final)/padj~log(maxvol)+mean_temp+site*(mu.scalar+I(mu.scalar^2))*(k.scalar+I(k.scalar^2)), family=poisson, data=noargco123datatemp)
# mfullb<-glm((bacteria.per.nl.final)/padj~log(maxvol)+mean_temp+site+(mu.scalar+I(mu.scalar^2))*(k.scalar+I(k.scalar^2)), family=poisson, data=noargco123datatemp)
# anova(mfulla, mfullb, test="Chisq")#adding mean temp means conting no longer sig
# Anova(mfullb, type=2) #BUT...meantemp itself not sig, but rainfall still sig...hmmm, not like mean temp is local direct effect
# mfullc<-glm((bacteria.per.nl.final)/padj~log(maxvol)+mean_temp+site, family=poisson, data=noargco123datatemp)
# Anova(mfullc, type=2)
# anova(mfulla, mfullc, test="Chisq") #confirming that mean temp didnt get rid of rainfall effect, ths is why failed hydrosum fn
# mfulld<-glm((bacteria.per.nl.final)/padj~log(maxvol)+mean_temp+I(mean_temp^2), family=poisson, data=noargco123datatemp)
# anova(mfullc, mfulld, test="Chisq")
# Anova(mfulld, type = 2) # quadratic super sig
# visreg(mfulld, "mean_temp", ylab="bacterial density")
# mfulle<-glm((bacteria.per.nl.final)/padj~log(maxvol)+(mean_temp+I(mean_temp^2))*(mu.scalar+I(mu.scalar^2))*(k.scalar+I(k.scalar^2)), family=poisson, data=noargco123datatemp)
# Anova(mfulle, type = 2)
# anova(mfulle, mfull, test="Chisq") #the quadratic effects of mean temp have not erased site effect
# mfullf<-glm((bacteria.per.nl.final)/padj~log(maxvol)+(mu.scalar+I(mu.scalar^2))*(k.scalar+I(k.scalar^2)), family=poisson, data=noargco123datatemp)
#
# anova(mfulle, mfullf, test="Chisq")#but mean temp quaratic explains (563-168)/(563-79) = 81% of the site effect
# mfullg<-glm((bacteria.per.nl.final)/padj~log(maxvol)+(mean_temp+I(mean_temp^2))*(mu.scalar+I(mu.scalar^2))+(k.scalar+I(k.scalar^2)), family=poisson, data=noargco123datatemp)
# anova(mfulle, mfullg, test="Chisq")#conclusion: mean temp doesn't get rid of contingency, it is really explaining the site effect.
# noargco123full<-filter(noargco123datatemp, bacteria.per.nl.final%nin%NA)
# noargco123full$resid.bacteria<-resid(glm(bacteria.per.nl.final/3.3~log(maxvol)+site, family=poisson,data=noargco123full))
# mfullh<-glm((resid.bacteria)^0.5~cv_mean_temp+(mean_temp+I(mean_temp^2))+site*(mu.scalar+I(mu.scalar^2))+(k.scalar+I(k.scalar^2)), family=gaussian, data=noargco123full)
# par(mfrow= c(2,2))
# plot(mfullh)
# Anova(mfullh, type=2) # a site x mu2 still significant, not any temp
# #optimal bacterial density at 22.5 oC
#
# noargprdatatemp<-filter(noargprdata, mean_temp%nin%NA)
# mco2<-glm(log(co2.final)~log(maxvol)+site*(mu.scalar+I(mu.scalar^2))*(k.scalar+I(k.scalar^2)), family=gaussian, data=noargprdatatemp)
# Anova(mco2,type=2)
# mco2norain<-glm(log(co2.final)~log(maxvol)+site, family=gaussian, data=noargprdatatemp)
# anova(mco2, mco2norain, test="Chisq")#no effect of rain, confired
# mco2noraina<-glm(log(co2.final)~log(maxvol)+mean_temp, family=gaussian, data=noargprdatatemp)
# anova(mco2noraina, mco2norain, test="Chisq")#mean_temp explains site effect...Co2 increases with mean temp
# mco2norainb<-glm(log(co2.final)~log(maxvol), family=gaussian, data=noargprdatatemp)
# anova(mco2norain, mco2norainb, test="Chisq") # (80.12-18.24)/(80.12-18.948) 100% of site effect explained
# plot(log(co2.final)~mean_temp, data= noargprdatatemp)
# plot(mean_temp~site, data= noargprdatatemp)
# noargprfull<-filter(noargprdatatemp, co2.final%nin%NA)
# noargprfull$resid.co2<-resid(glm(log(co2.final)~log(maxvol)+site, family=gaussian,data=noargprdatatemp))
# mco2a<-glm(resid.co2~cv_mean_temp*site+mean_temp*site+site*(mu.scalar+I(mu.scalar^2))+(k.scalar+I(k.scalar^2)), family=gaussian, data=noargprfull)
# par(mfrow= c(2,2))
# plot(mco2a)
# Anova(mco2a, type=2)#nothing sig
# mco2a<-glm(resid.co2~site*totalbio+site*bacteria.per.nl.final, family=gaussian, data=noargprfull)
#
#
#
# mdecomp<-glm(sqrt(decomp)~log(maxvol)+site*(mu.scalar+I(mu.scalar^2))*(k.scalar+I(k.scalar^2)), family=gaussian, data=temptruedata)
# mdecomp.noint<-glm(sqrt(decomp)~log(maxvol)+site+(mu.scalar+I(mu.scalar^2))*(k.scalar+I(k.scalar^2)), family=gaussian, data=temptruedata)
# Anova(mdecomp,type=3) #hmmm, sig conting with type 2 that disappears with type 3....
# mdecompnorain<-glm(sqrt(decomp)~log(maxvol)+site, family=gaussian, data=temptruedata)
# anova(mdecomp, mdecompnorain, test="Chisq")#no effect of rain, confirmed, but p =0.10 (thats why so close)
# anova(mdecomp, mdecomp.noint, test="Chisq")
# mdecompnoraina<-glm(sqrt(decomp)~log(maxvol)+mean_temp, family=gaussian, data=temptruedata)
# Anova(mdecompnoraina, type=2) #temp sig
# anova(mdecompnoraina, mdecompnorain, test="Chisq")#but site still does a better job.
# mdecompnorainb<-glm(sqrt(decomp)~log(maxvol), family=gaussian, data=temptruedata)
# anova(mdecompnorainb, mdecompnorain,test="Chisq") #mean temp only explains 7% of site effect: (4.121-4.3944)/ (4.3944-0.6044)
# plot(sqrt(decomp)~mean_temp, data= noargprdatatemp)
# plot(sqrt(decomp)~site, data= fulldata)#much of site variance may be due to litter species chosen
# temptruedata$resid.decomp<-resid(glm(sqrt(decomp)~log(maxvol)+site, family=gaussian,data=temptruedata))
# mdecompb<-glm((resid.decomp)^0.33~cv_mean_temp*site+mean_temp*site+site*(mu.scalar+I(mu.scalar^2))+(k.scalar+I(k.scalar^2)), family=gaussian, data=temptruedata)
# par(mfrow= c(2,2))
# plot(mdecompb)#perfect
# Anova(mdecompb, type=3)#now site x temp effects ...for most sites, decomp increase with temperature
# mdecompb<-glm((resid.decomp)^0.33~cv_mean_temp+mean_temp+bacteria.per.nl.final+detritivore_bio, family=gaussian, data=temptruedata)
# par(mfrow= c(2,2))
# plot(mdecompb)#perfect
# Anova(mdecompb, type=2)#only site; no overall effect of mean temp, seems to require site in the model...spurious?
# visreg(mdecompb, "mean_temp",by = "site", data= temptruedata)
# par(mfrow= c(1,1))
# plot(decomp~mean_temp, data=codata)#can't see any temp effects in any site
#
# #plot nitrogen? no126data$n15.bromeliad.final+4)^0.125
# mnit<-glm((n15.bromeliad.final+4)^0.125~log(maxvol)+site*(mu.scalar+I(mu.scalar^2))*(k.scalar+I(k.scalar^2)), family=gaussian, data=no126data)
# mnit2<-glm((n15.bromeliad.final+4)^0.125~log(maxvol)+site+(mu.scalar+I(mu.scalar^2))*(k.scalar+I(k.scalar^2)), family=gaussian, data=no126data)
# mnit3<-glm((n15.bromeliad.final+4)^0.125~log(maxvol)+site, family=gaussian, data=no126data)
# Anova(mnit, type=2)#no sig terms but..
# anova(mnit, mnit2, test="Chisq")#sig...hmmmm...
#
#
# #dont forget: grouped_by....do(tidy(lm(....)))